This PDF file contains the front matter associated with SPIE Proceedings Volume 9815, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Here, an efficient multi-objective automatic segmentation framework (MASF) is formulated and applied to synthetic aperture radar (SAR) image unsupervised classification. In the framework, three important issues are presented: 1) two reasonable image preprocessing techniques, including spatial filtering and watershed operator, are discussed at the initial stage of the framework; 2)then, an efficient immune multi-objective optimization algorithm with uniform clone, adaptive selection by online nondominated solutions, and dynamic deletion in diversity maintenance is proposed; 3 two very simple, but very efficient conflicting clustering validity indices are incorporated into the framework and simultaneously optimized. Two simulated SAR data and two complicated real images are used to quantitatively validate its effectiveness. In addition, four other state-of-the-art image segmentation methods are employed for comparison.

This paper attempts to develop an unsupervised learning approach for airplane detection in remote sensing images. This novel airplane detection method is based on circle-frequency filter and cluster-based co-saliency detection. Firstly, the CF-filter method is utilized as the coarse detection to detect target airplanes with some false alarms. Then, we collect all the detected targets and use cluster-based co-saliency detection to enhance the real airplanes and weaken the false alarms, so that most of the false alarms can be eliminated. Experimental results on real remote sensing images demonstrate the efficiency and accuracy of the proposed method.

Hyperspectral image visualization reduces high-dimensional spectral bands to three color channels, which are sought in order to explain well the nonlinear data characteristics that are hidden in the high-dimensional spectral bands. Despite the surge in the linear visualization techniques, the development of nonlinear visualization has been limited. The paper presents a new technique for visualization of hyperspectral image using t-distributed stochastic neighbor embedding, called VHI-tSNE, which learns a nonlinear mapping between the high-dimensional spectral space and the three-dimensional color space. VHI-tSNE transforms hyperspectral data into bilateral probability similarities, and employs a heavy-tailed distribution in three-dimensional color space to alleviate the crowding problem and optimization problem in SNE technique. We evaluate the performance of VHI-tSNE in experiments on several hyperspectral imageries, in which we compare it to the performance of other state-of-art techniques. The results of experiments demonstrated the strength of the proposed technique.

In order to meet the rapid positioning of 12315 complaints, aiming at the natural language expression of telephone complaints, a semantic retrieval framework is proposed which is based on natural language parsing and geographical names ontology reasoning. Among them, a search result ranking and recommended algorithms is proposed which is regarding both geo-name conceptual similarity and spatial geometry relation similarity. The experiments show that this method can assist the operator to quickly find location of 12,315 complaints, increased industry and commerce customer satisfaction.

This paper presents our research on automatic shadow detection from high-resolution aerial image through the hybrid analysis of RGB and HIS color space. To this end, the spectral characteristics of shadow are firstly discussed and three kinds of spectral components including the difference between normalized blue and normalized red component - BR, intensity and saturation components are selected as criterions to obtain initial segmentation of shadow region (called primary segmentation). After that, within the normalized RGB color space and HIS color space, the shadow region is extracted again (called auxiliary segmentation) using the OTSU operation, respectively. Finally, the primary segmentation and auxiliary segmentation are combined through a logical AND-connection operation to obtain reliable shadow region. In this step, small shadow areas are removed from combined shadow region and morphological algorithms are apply to fill small holes as well. The experimental results show that the proposed approach can effectively detect the shadow region from high-resolution aerial image and in high degree of automaton.

As many variable length source coders, JPEG-LS is highly vulnerable to channel errors which occur in the transmission of remote sensing images. The error diffusion is one of the important factors which infect its robustness. The common method of improving the error resilience of JPEG-LS is dividing the image into many strips or blocks, and then coding each of them independently, but this method reduces the coding efficiency. In this paper, a block based JPEP-LS lossless compression method with an adaptive parameter is proposed. In the modified scheme, the threshold parameter RESET is adapted to an image and the compression efficiency is close to that of the conventional JPEG-LS.

In order to solve the problems that image’s entropy of information decline obviously and boundary line phenomenon appear obviously in the processing of aerial remote sensing image’s mosaic, an image mosaic approach is presented in this paper, which uses wavelet image fusion based on structure similarity and is capable of creating seamless mosaics in real-time. The approach consists of three steps. First, the overlapping area of two aerial images is extracted. Then, the two overlapping area images are fused adaptively by the method of multi-layer wavelet decomposition based on the structure similarity and appointed regulation. Finally, weighted average fusion is used again to avoid the visible boundary line for the both sides of the boundary of the above fusion image. Experimental results show the entropy of information, sharpness and standard deviation have been improved significantly, and the boundary line has been eliminated observably.

In this paper a web information extraction method is presented which identifies a variety of thematic events utilizing the event knowledge framework derived from text training, and then further uses the syntactic analysis to extract the event key information. The method which combines the text semantic information and domain knowledge of the event makes the extraction of information people interested more accurate. In this paper, web based earthquake news extraction is taken as an example. The paper firstly briefs the overall approaches, and then details the key algorithm and experiments of seismic events extraction. Finally, this paper conducts accuracy analysis and evaluation experiments which demonstrate that the proposed method is a promising way of hot events mining.

Axis-shift multi-camera has been gradually applied in the aerial photogrammetry because of its advantages on structure design. In this paper, the basic axis-shift theory is analyzed, and an improved calibration method is described. A prototype system, including two axis-shift cameras, is developed to validate the feasibility and correctness of the proposed method. With the help of a high-precision indoor control field, the parameters of single camera and the relative orientation parameters of the dual camera system are calculated respectively. Experiment result indicates that this calibration method is suitable for the axis-shift multi camera system.

Location-related data are playing an increasingly irreplaceable role in business, government and scientific research. At the same time, the amount and types of data are rapidly increasing. It is a challenge how to quickly find required information from this rapidly growing volume of data, as well as how to efficiently provide different levels of geospatial data to users. This paper puts forward a data-oriented access model for geographic information science data. First, we analyze the features of GIS data including traditional types such as vector and raster data and new types such as Volunteered Geographic Information (VGI). Taking into account these analyses, a classification scheme for geographic data is proposed and TRAFIE is introduced to describe the establishment of a multi-level model for geographic data. Based on this model, a multi-level, scalable access system for geospatial information is put forward. Users can select different levels of data according to their concrete application needs. Pull-based and push-based data access mechanisms based on this model are presented. A Service Oriented Architecture (SOA) was chosen for the data processing. The model of this study has been described by providing decision-making process of government departments with a simulation of fire disaster data collection. The use case shows this data model and the data provision system is flexible and has good adaptability.

Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

Paddy is one of the most important food crops in China. Due to the intensive planting in the surrounding of rivers and lakes, paddy is vulnerable to flooding stress. The research on predicting crop yield loss derived from flooding stress will help the adjustment of crop planting structure and the claims of agricultural insurance. The paper aimed to develop a method of estimating yield loss of paddy derived from flooding by multi-temporal HJ CCD images. At first, the water pixels after flooding were extracted, from which the water line (WL) of turbid water pixels was generated. Secondly, the water turbidity index (WTI) and perpendicular vegetation index (PVI) was defined and calculated. By analyzing the relation among WTI, PVI and paddy yield, the model of evaluating yield loss of flooding was developed. Based on this model, the spatial distribution of paddy yield loss derived from flooding was mapped in the study area. Results showed that the water turbidity index (WTI) could be used to monitor the sediment content of flood, which was closely related to the plant physiology and per unit area yield of paddy. The PVI was the good indicator of paddy yield with significant correlation (0.965). So the PVI could be used to estimate the per unit area yield before harvesting. The PVI and WTI had good linear relation, which could provide an effective, practical and feasible method for monitoring yield loss of waterlogged paddy.

In recent years, the occurrence of large earthquakes is frequent all over the word. In the face of the inevitable natural disasters, the prediction of the earthquake is particularly important to avoid more loss of life and property. Many achievements in the field of predict earthquake from remote sensing images have been obtained in the last few decades. But the traditional prediction methods presented do have the limitations of can't forecast epicenter location accurately and automatically. In order to solve the problem, a new predicting earthquakes method based on extract the texture and emergence frequency of the earthquake cloud is proposed in this paper. First, strengthen the infrared cloud images. Second, extract the texture feature vector of each pixel. Then, classified those pixels and converted to several small suspected area. Finally, tracking the suspected area and estimate the possible location. The inversion experiment of Ludian earthquake show that this approach can forecast the seismic center feasible and accurately.

In order to fully understand the dynamic range of Gaofen-2 satellite data and support the data processing, application and next satellites development, in this article, we evaluated the dynamic range by calculating some statistics such as maximum ,minimum, average and stand deviation of four images obtained at the same time by Gaofen-2 dual cameras in Beijing area; then the maximum ,minimum, average and stand deviation of each longitudinal overlap of PMS1,PMS2 were calculated respectively for the evaluation of each camera's dynamic range consistency; and these four statistics of each latitudinal overlap of PMS1,PMS2 were calculated respectively for the evaluation of the dynamic range consistency between PMS1 and PMS2 at last. The results suggest that there is a wide dynamic range of DN value in the image obtained by PMS1 and PMS2 which contains rich information of ground objects; in general, the consistency of dynamic range between the single camera images is in close agreement, but also a little difference, so do the dual cameras. The consistency of dynamic range between the single camera images is better than the dual cameras’.

Synthetic aperture radar (SAR) plays an important role in measuring directional ocean wave spectra with continuous and global coverage. In this article, satellite SAR images were used to estimate the wave parameters in the East China Sea. The Max-Planck Institut (MPI) method was applied to retrieve directional wave spectra from the SAR imagers with the Simulating WAves Nearshore (SWAN) model data as the first guess wave spectra. In order to validate the SAR retrieved wave spectra, a set of buoy measurements during the SAR imaging times was collected and used. The SAR retrieved significant wave heights (SWHs) were analyzed against the buoy measurements to assess the wave retrieval of this study. The root-mean-square error between the SAR SWHs and the buoy measurements is 0.25 m, which corresponds to a relative error of 12%. The case study here shows that the SWAN model data is a potential first guess wave spectra source to the MPI method to retrieve ocean wave spectra from SAR imagery.

In this paper, we propose a novel remote sensing fusion approach based on guided image filtering. The fused images can well preserve the spectral features of the original multispectral (MS) images, meanwhile, enhance the spatial details information. Four quality assessment indexes are also introduced to evaluate the fusion effect when compared with other fusion methods. Experiments carried out on Gaofen-2, QuickBird, WorldView-2 and Landsat-8 images. And the results show an excellent performance of the proposed method.

As the important technology of remote sensing, surface target detection aims to obtain the information of the surface target, such as water, construction, vegetation and other interesting targets, through the remote sensing image processing and analysis. However, the pre-collection samples of some targets from the single source images are too few to meet the needs of automatic detection in the multi-scale remote sensing images, so target detection is still a challenge. Focus on the problem, a novel target detection method based on transfer learning using multiple sources for surface target in the remote sensing images is proposed. The most remarkable characteristic of transfer learning is that it can employ the knowledge in relative domains to help perform the learning tasks in the domain of the target. With the use of different sources of knowledge, transfer learning can transfer and share the information between similar domains. The proposed method locates the surface target area firstly, and then makes the target samples from different sources involved in learning. Therefore, the similar knowledge conductive to the target can be obtained. The prior knowledge from the multiple sources is transferred to the new target images for target detection. The experimental results show that effect of surface target detection by the proposed method from multiple sources is better than that from the single source, and the accuracy of detection has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the advantage of our method in the multiple sources.

Based on the spatial autocorrelation (SA) and Geographic Information System (GIS) technology, the spatial differences of the rural economic development level was investigated by adopting the rural resident income data in Guizhou Province from 2009 to 2013. The results of global Moran’s I value for rural resident income at county level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident income. However, the global Moran’s I value showed the increasing trend during 2009 to 2013, indicating an enlarged spatial disparity of rural economy in Guizhou. The results of the local indicators of spatial association(LISA) cluster maps of 2009 and 2013 showed that most of counties were characterized by positive local spatial association , ie. They were located in the HH or the LL region. The significant HH counties were mostly to be found in the middle part of Guizhou. The significant LL counties were found in the Wuchuan county and Zhenfeng county, northwest and southwest of Guizhou.

An improved Dark Object Subtraction (DOS) method was introduced for Landsat 8 multispectral satellite image in this paper. The main factors including Rayleigh scattering, Mie scattering in path radiance, as well as the other satellite image parameters (such as height modification, slope distance and azimuth), ware considered in the algorithm. The algorithm consists of three steps. First, starting band haze values are selected using histogram of a single image. Then predicted haze values were calculated using a known scattering model and the multispectral normalized gains and offset values. Finally, final predicted haze values are obtained by the predicted haze values and haze values. Compared with other improved Dark Object Subtraction methods, the result of this algorithm is more realistic on geographical object recognition on NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) data.

This paper presents a novel Object-based context sensitive technique for unsupervised change detection in very high spatial resolution(VHR) remote sensing images. The proposed technique models the scene at different segment levels defining multiscale-level image objects. Multiscale-level image object change features is helpful for improving the discriminability between the changed class and unchanged class. Firstly according to the best classification principle as “homogeneity in class, heterogeneity between class”, A set of optimal scales are determined. Then a multiscale level change vector analysis to each pixel of the considered images helps improve the accuracy and the degree of automation, which is implemented on multiscale features fusion. The technique properly analyzes the multiscale-level image objects’ context information of the considered spatial position. The adaptive nature of optimal multiscale image objects and their multilevel representation allow one a proper modeling of complex scene in the investigated region. Experimental results confirm the effectiveness of the proposed approach.

Rice-planted area and production monitoring has significance for governments to formulate some food related policy. Remote sensing has an obvious advantage for the rice monitoring. As for the rice-planted area, the special growth raw shows different feature in the remote sensing image. In this paper, the multi-temporal Landsat-8 OLI image of Menghun and Mengzhe town in Xishuangbanna autonomous prefecture where planting a large number of rice was used as the test data, the corresponding changes of the difference between NDVI and NDWI was used as the diagnostic feature, and the SAM classification approach was introduced to extract rice-planted area. The experiments shows that the approach could acquire more than 95% of the extraction accuracy.

In the paper, the use of 1994 and 2005 Landsat TM data monitors the land use and changes of the Shiyang river basin with remote sensing classification comparison under the support of GIS. The result shows that from 1994 to 2005, the area of farmland and settlement land increased greatly and that of grass and forest reduces obviously in this basin; Minqin which is in the lower reaches of Shiyang river basin has a serious desertification with low grass coverage degradation into desert. With a comprehensive analysis about the driving factors of nature and human, population pressure and irrational use of water resource are the key factors of the Land use change and between the natural factor and human factor, human acts leading role.

Coastal zone is the interaction area between the ocean and the land, and it is one of the most important residential areas of human. Coastal area management and planning is necessary in utilizing coastal space and resources. Coastal zone changed rapidly in recent decades in Tangshan, China. In this research, a total of 11 Landsat images were selected for studying the coastal change in Tangshan during the last 35 years. Results showed that the coastline length increased by 114.05 km, while land area increased by 449.76km² from 1975 to 2010. The main period of coastal increasing in Tangshan occurred during 2005–2010, and the primary area changes happened in Caofeidian District and Jingtang Port. The main reason of the rapid coastal changes in Tangshan was the human activities of industrial and commercial district construction and harbor construction.

Urban impervious surface is not only a major indicator of urbanization but also a key factor for environmental quality evaluation. NDISI is applied for enhancing and extracting impervious surface information in this study. NDVI derived from measurements of the optical imagery reflectance of sunlight in the red and near-infrared wavelengths, can reveal the vegetation cover condition. LST plays an important role in energy exchange between the land surface and the atmosphere. The objective of this paper is to extract the urban impervious surface information and its eco-envionment effect. Experiment results indicate that NDISI is effective and easy-to-use. And the correlation coefficient among NDISI, NDVI and LST are -0.82, 0.95 and -0.7. Combined with the field survey meteorological site data, this study reveals that the urban impervious surface area has a strong positive exponential relationship with the LST. This suggests that the areas with high NDISI value will accelerate the increase in LST much more than the areas with low NDISI value. And urban impervious surface area is the most important factor contributing to the development of the UHI while vegetation cover (high NDVI area) can significantly reduce the LST.

Image segmentation is the foundation of seismic information extraction from high-resolution remote sensing images. While the complexity of the seismic image brings great challenges to its segmentation. Compared with the traditional pixel-level approaches, the region-level approaches are found prevailing in dealing with the complexity. This paper addresses the seismic image segmentation problem in a region-merging style. Starting from many over-segmented regions, the image segmentation is performed by iteratively merging the neighboring regions. In the proposed algorithm, the merging criterion and merging order are two essential issues to be emphatically considered. An effective merging criterion is largely depends on the region feature and neighbor homogeneity measure. The region’s spectral histogram represents the global feature of each region and enhances the discriminability of neighboring regions. Therefore, we utilize it to solve the merging criterion. Under a certain the merging criterion, a better performance could be obtained if the most similar regions are always ensured to be merged first, which can be transformed into a least-cost problem. Rather than predefine an order queue, we solve the order problem with a dynamic scheme. The proposed approach mainly contains three parts. Firstly, starting from the over-segmented regions, the spectral histograms are constructed to represent each region. Then, we use the homogeneity that combines the distance and shape measure to conduct the merge criterion. Finally, neighbor regions are dynamically merged following the dynamic program (DP) theory and breadth-first strategy. Experiments are conducted using the earthquake images, including collapsed buildings and seismic secondary geological disaster. The experimental results show that, the proposed method segments the seismic image more correctly.

Speckle filtering seems to be a never-ending topic for polarimetric synthetic aperture radar imagery processing. Constantly emerging literatures demonstrate that this issue deserves further research effort, especially in the context of much more high spatial resolution. A comparative study will be performed in this paper for recently proposed method such as non-local SAR speckle filtering, Extended Sigma filter proposed by Lee, non-local means filter, Bilateral filter, and so on. Their performance on spatial details preserving and polarimetric properties preserving should be measured thoroughly. Further more the computing performance on large-scale dataset should also be measured.

This paper analyses the forest landscape diversity of the study area with the help of ArcGIS10 and GS+ software. The forest landscape diversity and spatial interpolation and spatial differentiation are also carried out. The result shows that the maximum value of SHDI in 1997is 2.0463 and the minimum value is 0.2544 , which are 1.9722 and 0.2418 in the year of 2009. The advantage religion of SHDI mainly distributes in the middle of the study region , showing a band region from southwest to northeast . The forest landscape diversity and the space location have a moderate spatial correlation and a obvious structural under a forest level.

With the rapid development of earth observation technology and the rapid increase of remote sensors, the amount of remote sensing data rapidly expands, which leads to increasingly outstanding contradiction of “more and less” remote sensing data. According to the user’s preference characteristics, intelligent distribution of remote sensing data is an effective solution to the problem. The user profile model is the basis of intelligent distribution of remote sensing data. This paper intends to (1) establish the user profile model which describes the user’s preference characteristics of remote sensing data; (2) develop similarity measure methods of user profile models, according to which similarities of user preference characteristics can be calculated; (3) put forward prediction methods of the user’s ratings of remote sensing data, based on which remote sensing data can be collaboratively distributed.

In order to solve the problem of in-shore ship extraction from remote sensing image, a novel method for in-shore ship extraction from high resolution (HR) optical remote sensing image is proposed via salience structure feature and GIS information. Firstly, the berth ROI is located in the image with the aid of the prior GIS auxiliary information. Secondly, the salient corner features at ship bow are extracted from the berth ROI precisely. Finally, a recursive algorithm concerning the symmetric geometry of the ship target is conducted to discriminate the multi docked in-shore targets into mono in-shore ships. The results of the experiments show that the method proposed in this paper can detect the majority of large and medium scale in-shore ships from the optical remote sensing image, including both the mono and the multi adjacent docked in-shore ship cases.

Photogrammetry is the study of information by using image acquisition, processing, extraction and results in expression of a craft, science and technology. The photogrammetry can be used to test the various scales of topographic maps[1]. Photogrammetry has become a discipline intersection of righteousness and computer technology. The expensive equipment that photography measurement requires has gradually been replaced by computer and its related input and output devices. Particularly , the integration of photogrammetry technology with GPS, LIDAR and GIS, photogrammetry has become a professional required course. Practice is a bottleneck that restricts the teaching effect of Photogrammetry. Its content is a basis of new technology such as UAV mapping and oblique photogrammetry. In 2007 the Ministry of Education officially launched the undergraduate teaching quality and teaching reform project. The colleges and universities in China have actively carried out the reform and practice of applied and innovative talents training and explore the mode and way of cultivating new talents. To effectively improve the effect of practice and innovation ability of student, Heilongjiang institute of technology has constructed the experimental platform of simulation and emulation of photogrammetry, so that the students can complete the practice in simulation environment, explore the new mode of photogrammetric practice, and improve the quality of personnel training.

In recent years, with the rapid development of China's satellite remote sensing technology, ZY-1 02C, ZY-3 and TH-1 satellites have been successfully launched, ZY-3 satellite is China's first autonomous civilian high-resolution stereo mapping satellite, achieved a breakthrough in the field of civil high resolution mapping satellite[1]. Its successful applications have become a new milestone in the history of Chinese satellite surveying and mapping, undertake to build database of remote sensing information, promote the development of geographic space information industry. This paper, based on data distribution service subsystem of the construction of ZY-3 ground processing as an example, introduces GIS in the subsystem which plays an important role. This sub-system is the window of the ground system of information collection and product distribution, whose task is to provide ZY-3, ZY-1 02C a variety of sensor data distribution service at all levels of products, to provide users with a unified search, browse, order and download services, and has a certain capacity expansion upgrade, which provides a technical basis and guarantee for subsequent satellite distribution service. With ZY-3 satellite in orbit, the amount of data is increasing, how to efficiently manage multi-source image data becomes the system to be solved. In this paper, ArcGIS mosaic datasets is used to manage large-scale image data to solve the many problems that exist in the traditional image management and shared services to complete data distribution. At present, the distribution system has been stabilized, and serves the masses of users.

Cited a measuring operation for determining the geometric characteristics of objects in space and geodetic survey objects on the ground. In the course of the work, data were obtained on a relative positioning of the pylons in space. There are deviations from verticality. In comparison with traditional surveying this testing method is preferable because it allows you to get in semi-automated mode, the CAD model of the object is high for subsequent analysis that is more economical-ly advantageous.

Flood is one of natural disasters with the worst loss in the world. It needs to assess flood disaster risk so that we can reduce the loss of flood disaster. Disaster management practical work needs the dynamic risk results of building. Rainstorm flood disaster system is a typical complex system. From the view of complex system theory, flood disaster risk is the interaction result of hazard effect objects, rainstorm flood hazard factors, and hazard environments. Agent-based modeling (ABM) is an important tool for complex system modeling. Rainstorm-flood building risk dynamic assessment method (RFBRDAM) was proposed using ABM in this paper. The interior structures and procedures of different agents in proposed meth had been designed. On the Netlogo platform, the proposed method was implemented to assess the building risk changes of the rainstorm flood disaster in the Huaihe River Basin using Agent-based simulation (ABS). The results indicated that the proposed method can dynamically assess building risk of the whole process for the rainstorm flood disaster. The results of this paper can provide one new approach for flood disaster building risk dynamic assessment and flood disaster management.

Icing thickness parameter is the basic data for power sector to make decision for icing accident prevention. In this paper, a transmission line icing measurement method is proposed. It used the photogrammetry method to realize icing parameters measurement through the integration of high resolution camera, laser range finder and inertial measurement unit. Compared with traditional icing measurement method, this method is flexible and is the effective supplement of the fixed icing detection terminal. And its high accuracy measurement guarantees the reliability of the icing thickness parameters.

Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik, total variation and adaptive total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. In this paper, We propose a novel Spatially Adaptive Guide-Filtering Total Variation (SAGFTV) regularization with image restoration algorithm for denoising images. The guide-filter is extended to the variational formulations of imaging problem, and the spatially adaptive operator can easily distinguish flat areas from texture areas. Our simulating experiments show the improvement of peak signal noise ratio (PSNR), root mean square error (RMSE) and structure similarity increment measurement (SSIM) over other prior algorithms. The results of both simulating and practical experiments are more appealing visually. This type of processing can be used for a variety of tasks in PDE-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.

The means to get the accurate high-orbit satellite magnitude can be significant in space target surveillance. This paper proposes a satellite photometric measurement method based on image processing. We calculate the satellite magnitude by comparing the output value of camera’s CCD between the known fixed star and the satellite. We calculate the luminance value of a certain object on the acquired image using a background-removing method. According to the observation parameters such as azimuth, elevation, height and the situation of the telescope, we can draw the star map on the image, so we can get the real magnitude of a certain fixed star in the image. We derive a new method to calculate the magnitude value of a certain satellite according to the magnitude of the fixed star in the image. To guarantee the algorithm’s stability, we evaluate the measurement precision of the method, and analysis the restrict condition in actual application. We have made plenty of experiment of our system using large telescope in satellite surveillance, and testify the correctness of the algorithm. The experimental result shows that the precision of the proposed algorithm in satellite magnitude measurement is 0.24mv, and this method can be generalized to other relative fields.

The algorithm of single image haze removal using dark channel prior has a good result on restoring hazy images and makes it clear. But the original algorithm has some disadvantages such as inaccurate estimation of atmospheric light and distortion of the sky region. From the perspective of the two problems referred previously, we propose a new method to estimate the transmission and atmosphere light. First, according to the characteristics of the sky region, we divide the sky region and get the accurate atmospheric light; then, in order to avoid the distortion of the sky, a controllable parameter K is introduced to the transmission. The experiment results show that the restored images acquired by the experiment have natural colors and clear details.

In the application of image processing and pattern recognition, the precision of image preprocessing has a great influence on the image after-processing and analysis. This paper describes a novel local double mean weighted algorithm (hereinafter referred to as D-M algorithm) for image denoising. Firstly, the pixel difference and the absolute value are taken for the current pixels and the pixels in the neighborhood; then the absolute values are sorted again, the means of such pixels are taken in an half-to-half way; finally the weighting coefficient of the mean is taken. According to a large number of experiments, such algorithm not only introduces a certain robustness, but also improves increment significantly.

A new Ku-band low incidence model (KuLMOD) is proposed for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) buoy-measured wind and wave data. The TRMM PR data properties were analyzed regarding their dependence on the wind speed. The KuLMOD model was developed using incidence angles (0.5–6.5°) and wind speeds (1.5–16.5 m/s) as inputs. The model coefficients were derived by fitting the collocated data. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the NDBC buoy measurements, yielding a root mean square error of 1.57 m/s. The retrieval accuracies for the different incidence angles and wind speeds are presented.

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Ms8.0 Wenchuan earthquake that occurred on May 12, 2008 brought huge casualties and property losses to the Chinese people, and Beichuan County was destroyed in the earthquake. In order to leave a site for commemorate of the people, and for science propaganda and research of earthquake science, Beichuan National Earthquake Ruins Museum has been built on the ruins of Beichuan county. Based on the demand for digital preservation of the earthquake ruins park and collection of earthquake damage assessment of research and data needs, we set up a data set of Beichuan National Earthquake Ruins Museum, including satellite remote sensing image, airborne remote sensing image, ground photogrammetry data and ground acquisition data. At the same time, in order to make a better service for earthquake science research, we design the sharing ideas and schemes for this scientific data set.

The idea of using skylight polarization in navigation is learned from animals such as desert ants and honeybees. Various research groups have been working on the development of novel navigation systems inspired by polarized skylight. The research of background in polarized skylight navigation is introduced, and basic principle of the insects navigation is expatiated. Then, the research progress status at home and abroad in skylight polarization pattern, three bio-inspired polarized skylight navigation sensors and polarized skylight navigation are reviewed. Finally, the research focuses in the field of polarized skylight navigation are analyzed. At the same time, the trend of development and prospect in the future are predicted. It is believed that the review is helpful to people understand polarized skylight navigation and polarized skylight navigation sensors.

Using the MOD13A3 data from 2000 to 2013, Analyze the change of time and space by using the methods of maximum synthesis, trend line analysis, and Hurst index; at the same time, calculate and analyze the vegetation coverage in Hexi area with dimidiate pixel model. The result shows an obvious and overall increasing trend of the vegetation index in Hexi area, Gansu province from 2000 to 2013; the area with strong sustainable variation is the largest as a percentage of the total Hexi area, which is 18.1%, the area with strong anti-sustainable variation is only 0.2% as a percentage of the total Hexi area; the high and medium vegetation coverage areas in 2013 has increased than that in 2000,the association between the variation of high vegetation coverage area in Hexi and annual precipitation is smaller, and generally, the vegetation coverage area in Hexi, Gansu province has a subtle increasing trend.

The matching area selection is the foundation of gravity gradient aided navigation. In this paper, a gravity gradient matching area selection criterion is proposed, based on the principal component analysis (PCA) and analytic hierarchy process (AHP). Firstly, the features of gravity gradient are extracted and nine gravity gradient characteristic parameters are obtained. Secondly, combining PCA with AHP, a PA model is built and the nine characteristic parameters are fused based on it. At last, the gravity gradient matching area selection criterion is given. By using this criterion, gravity gradient area can be divided into matching area and non-matching area. The simulation results show that gravity gradient position effect in the selected matching area is superior to the matching area, and the matching rate is greater than 90%, the position error is less than a gravity gradient grid.

On Nov 19, 2012, HJ-1-C launched successfully, which is belong to Environment and Disaster Monitoring and Prediction Small Satellite Constellation, and is the first civil Synthetic Aperture Radar (SAR) satellite in China and the first successful SAR satellite of S band on-orbit operation in the world. During the on-orbit test period, National Disaster Reduction Center of China (NDRCC) preliminarily evaluated its disaster reduction application ability in the ice, flood, drought, snow, landslide and debris flow, etc. The results show that SAR satellite of S band has more highlight advantage than the HJ-1-A and HJ-1-B in the detailed characterization, and has well disaster reduction potential.

Visual surveillance system for law enforcement or police case investigation is different from traditional application, for it is designed to monitor pedestrians, vehicles or potential accidents. Visual surveillance risk is defined as uncertainty of visual information of targets and events monitored in present work and risk entropy is introduced to modeling the requirement of police surveillance task on quality and quantity of vide information. the prosed coverage model is applied to calculate the preset FoV position of PTZ camera.

The development of satellite remote sensing technology is rapid in China in recent years, multiple optical remote sensing satellites have been launched like HJ-1A&B,ZY-1 02C,ZY-3,GF-1/2 and so on, which provide rich remote sensing data source for lithologic mapping. In order to give full play to the advantages of domestic satellite data in lithologic mapping and support data source selection of lithologic mapping, application product development, product and service quality assurance, meanwhile, promote domestic satellite application in geological mapping and improve the development of follow-up satellites, a region about 420km² in the East Tianshan Mountains, China was chosen as the study area, and a comparative evaluation of multiple satellite remote sensing data including HJ-1A, ZY-1 02C and ZY-3 in lithology interpretation was carried out in this paper. The applicable condition of these three satellite images were pointed out and the recommendations for future work were presented.

High Efficiency Video Coding (HEVC) is the state-of-art video compression standard which fully support scalability features and is able to generate layered video streams with unequal importance. Unfortunately, when the base layer (BL) which is more importance to the stream is lost during the transmission, the enhancement layer (EL) based on the base layer must be discarded by receiver. Obviously, using the same transmittal strategies for BL and EL is unreasonable. This paper proposed an unequal error protection (UEP) system using different hierarchical amplitude modulation (HQAM). The BL data with high priority are mapped into the most reliable HQAM mode and the EL data with low priority are mapped into HQAM mode with fast transmission efficiency. Simulations on scalable HEVC codec show that the proposed optimized video transmission system is more attractive than the traditional equal error protection (EEP) scheme because it effectively balances the transmission efficiency and reconstruction video quality.

Oil spills are one of the major environmental concerns, especially in the coastal zones of the ocean. Satellite remote sensing imagery has proved to be a useful tool for monitoring oil spills in the marine environment. With its two daily acquisitions and the possibility to obtain near-real-time data free of charge, the Moderate Resolution Imaging Spectroradiometer (MODIS) shows interesting potential as such a cost-effective supplementary tool. Several researches on oil spill detection in MODIS imagery has been carried out for the past few years. Basically, oil spills were manually detected from MODIS imagery [1,2]. The disadvantage of the manual detection method is inefficient and subjective. Shi et al. proposed an oil spill detection method from MODIS imagery by using fuzzy cluster and texture feature extraction [3]. It works in an automatic manner and does not require any priori knowledge of occurrence or the spectral attributes of spills. But its efficiency in very near shore regions is limited. Chen and Zhao detected oil spills from the oil-water contrast ratio image by using a thresholding method [4].They found that the oil-water contrast ratio can be enhanced by replacing the original image with the ratio image of two different band ones in 400-800 nm. To obtain the oil-water contrast ratio image from the MODIS imagery, they selected the oil spill area and the background sea area and then calculated the mean radiance or emissivity value in those areas. By doing so, the automation and the accuracy of the method were reduced. Adamo et al. [5] and Kudryavtsev et al. [6] proposed physical methods for oil spill detection from MODIS imagery acquired in sunglint conditions. These two methods take imaging geometry into consideration and have the aid of other models or functions such as the Cox and Munk (1954) model [7],the CMOD4 model [8,9], the ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric model, and the transfer function, which increase the algorithm complexity and rely on some assumptions.

With the development of image processing and computer vision, automatic target locating in computer vision has been matured and replaced the manual target locating in some fields. Complex background image localization has many important applications in traffic control and management, industrial assembly, industrial control, and other fields. In this paper, we built an image localization system based on Gradient Hough Transform applied in circular device detection and localization in industrial control. This system aimed to correcting the mechanical deviation by using image localization. This image localization system includes image preprocessing, edge extraction, circle detection, localization of the center of the circular device, deviation calculation and feedback. According to the experimental result, the accuracy of this method in the actual system less than 0.3mm, and the system can basically achieve the real-time detection.

In this paper, P5 stereo images and GeoEye-1 image are firstly processed and analysed in order to extract Digital Elevation Model (DEM) and divide stratum. The perpendicular Root Mean Square error of extracted DEM is within 5m in residual elevation report using methods of check point and visual inspection. According to the stratum division, measure points which are located at boundary or inner stratum are chosen at regions with better triangular facet of rock and st1able attitude, and then the attitude of stratum is extracted using three-dimensional data of the measure points through programming in MATLAB environment based on three-point method or multiple-point fitting method. Based on field survey, the measurement residual of dip angles is within 6 degrees, 85% less than 4 degrees. and those intermediate less than 3 degree. At the same time, seasons of residual generation are concluded, including geological structure, topography, accuracy of DEM, matching degree of DEM and images, the location of chosen point and so on. It is suggested that the method of attitude extraction using P5 stereo images and GeoEye-1 image is less restricted by worse natural conditions. Not only can the method restricts the profile of shallow tectonics, but also can cover the shortage of attitude of stratum, and have great significance for reducing uncertainty of Kalpin thrust tectonic deformation research. It provides a new way for quantitative research of Structural Geology, and also provides references for similar regions.

Affected by over-exploration of groundwater for a long time, the Hangjiahu Plain in Zhejiang province, southeast of China, has suffering serious ground subsidence during the past several decades. In this paper, we investigate the time series InSAR technique for the generation of subsidence maps over this plain. 25 Radarsat-2 images acquired from Jan 2012 to Nov 2014 are used. The results show that serious subsidence has taken place in the north and southeast of Jiaxing, the east and north of Huzhou, and the north of Hangzhou. Meanwhile some rebound occurs in the east of Jiaxing and the southeast of Huzhou. The results are compared with 35 levelling measurements. The standard deviation of the error between the two data is 3.01mm, which demonstrate that time series InSAR technique has good accuracy for subsidence monitoring.

An imaging system is researched for the shaft size measurement, thus to replace the on-line manual measuring method, which is used to measuring diametric sizes and axial sizes of the shaft. Through the research of the characteristics of illumination, a kind of backlight was designed, which could improve the quality of image. For one CCD camera to the large size of the shaft is not easy to achieve, to continue research two CCD cameras imaging, the use of two cameras shoot the shaft two ends, to reduce the field of view to improve accuracy. At the same time, using the drive device to the relative position of the two cameras to achieve measure a variety of specifications of the shaft, improve compatibility. Because of the shaft parts for curved surface, need to extract the characteristics are not in the same plane, the telecentric lens of large depth of field was selected, to ensure the accuracy of image information. The image processing based on HALCON. From the measurement results, the shaft size measurement system measuring accuracy is high.

Earthquake is one major nature disasters in the world. At 8:02 on 20 April 2013, a catastrophic earthquake with Ms 7.0 in surface wave magnitude occurred in Sichuan province, China. The epicenter of this earthquake located in the administrative region of Lushan County and this earthquake was named the Lushan earthquake. The Lushan earthquake caused heavy casualties and property losses in Sichuan province. After the earthquake, various emergency relief supplies must be transported to the affected areas. Transportation network is the basis for emergency relief supplies transportation and allocation. Thus, the road losses of the Lushan earthquake must be monitoring. The road losses monitoring results for Lushan earthquake disaster utilization multisource remote sensing images were reported in this paper. The road losses monitoring results indicated that there were 166 meters' national roads, 3707 meters' provincial roads, 3396 meters' county roads, 7254 meters' township roads, and 3943 meters' village roads were damaged during the Lushan earthquake disaster. The damaged roads mainly located at Lushan County, Baoxing County, Tianquan County, Yucheng County, Mingshan County, and Qionglai County. The results also can be used as a decision-making information source for the disaster management government in China.

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Considering the problem in monitoring agricultural condition in the semi-arid areas of Northwest of China, we propose a new method for estimation of crop planting area, using the single phase optical and microwave remote sensing data collaboratively, which have demonstrated their respective advantages in the extraction of surface features. In the model, the ASAR backscatter coefficient is normalized by the incident angle at first, then the classifier based on Bayesian network is developed, and the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. Moreover the crop planting areas can be extracted by the classification results. At last, the model is validated for the necessities of normalization by the incident angle and integration of TM and ASAR respectively. It results that the estimation accuracy of crop planting area of corn and other crops garden are 98.47% and 78.25% respectively using the proposed method, with an improvement of estimation accuracy of about 3.28% and 4.18% relative to single TM classification. These illustrate that synthesis of optical and microwave remote sensing data is efficient and potential in estimation crop planting area.

This paper mainly focuses on the digital demonstration of three gorges archeological relics to exhibit the achievements of the protective measures. A novel and effective method based on 3D-visualization technology, which includes large-scaled landscape reconstruction, virtual studio, and virtual panoramic roaming, etc, is proposed to create a digitized interactive demonstration system. The method contains three stages: pre-processing, 3D modeling and integration. Firstly, abundant archaeological information is classified according to its history and geographical information. Secondly, build up a 3D-model library with the technology of digital images processing and 3D modeling. Thirdly, use virtual reality technology to display the archaeological scenes and cultural relics vividly and realistically. The present work promotes the application of virtual reality to digital projects and enriches the content of digital archaeology.

In liquid crystal display (LCD) colorimetric characterization, it needs to convert RGB the device-dependent color space to CIEXYZ or CIELab the device-independent color space. Namely establishing the relationship between RGB and CIE using the data of device color and the corresponding data of CIE. Thus a color automatic message acquisition software is designed. We use openGL to fulfill the full screen display function, write c++ program and call the Eyeone equipment library functions to accomplish the equipment calibration, set the sample types, and realize functions such as sampling and preservation. The software can drive monitors or projectors display the set of sample colors automatically and collect the corresponding CIE values. The sample color of RGB values and the acquisition of CIE values can be stored in a text document, which is convenient for future extraction and analysis. Taking the cubic polynomial as an example, each channel is sampled of 17 sets using this system. And 100 sets of test data are also sampled. Using the least square method we can get the model. The average of color differences are around 2.4874, which is much lower than the CIE2000 commonly required level of 6.00.The successful implementation of the system saves the time of sample color data acquisition, and improves the efficiency of LCD colorimetric characterization.

We present a novel blind restoration algorithm to restore object images from real images based on maximum likelihood estimation. In order to estimate the unknown turbulence point spread functions (PSF) of the observed image contain the same objects, multi-frame images are used to construct an integral likelihood function. A series of experiments for synthetic and real images illustrate that the proposed algorithm is effective in restoring real images.

The purpose of this study is to perform the quantitative diagnosis of drought hazard degree using multi-source remote sensing data. Hazard degree is the basic function for disaster risk assessment and loss assessment. Quantitative diagnosis of drought hazard degree is essential to decision-making of drought early warning and emergency relief in practice. The currently used diagnosis methods are based on disaster loss and drought indices. The response process and impacts of drought in different crop growth stages were ignored in these methods. So, the instructions were not dynamic and real time. This study investigated the drought hazard degree diagnosing of winter wheat based on continuous multi-source remote sensing imagery and comprehensive ground-based observations. The resulted indicated that the correlation is high and drought hazard degree is suitable and sensitive to reveal drought disaster-forming environment evolution, drought formation mechanism and drought influence.

Experimental scheme was designed based on the steel production process, surface optical characteristics and BRDF (Bidirectional Reflectance Distribution Function) illumination model theory. The relationship between the light incidence angle, surface roughness and laws of light scattering under a particular light-source conditions were found through a series of light scattering characteristics experiments for different steel plate surface. The results showed that there was an apparent specular reflection peak on steel surface. surface light scattering was influenced greatly by light incidence angle and surface roughness, and it showed the law of exponential distribution functions. Thus the improved semi-empirical light scattering mathematical model which based on roughness factor and surface Gaussian distribution of micro-plane components has been formed through non-linear model fitting and optimization. The surface illumination model has been proposed to accurately describe the light intensity distribution of steel plate surface and provide a theoretical method for the design of optimal imaging system.

The ground subsidence phenomenon is more serious in Beijing, large-scale land subsidence seriously threats to urban planning and construction and the safety of residents. In order to study the subsidence condition, it is necessary to monitor land subsidence. Choosing 28 scenes Envisat ASAR images covering Beijing city from December 2003 to March 2009, permanent scatterer SAR interferometry (PSI) technique was applied to obtained time series land subsidence information. Then the trend characteristics and factors of subsidence were analyzed, comparing land subsidence result with the groundwater data and geological structure data. Comparison between the PSI-derived subsidence rates and leveling data obtained shows that the result of PSI is agreed with the leveling data. The results indicate that the PSI technique is capable of providing high-level accuracy subsidence information. The results show that:(1) The deformation rates derived PSI ranging from -45.80 to 4.36mm/a;(2) In the study area, the serious subsidence areas distribute in Chaoyang District, Shunyi District, Tongzhou District and Pinggu District;(3) The subsidence tends to become more and more concentrated in 6 years from 2003 to 2009.

Infrared spectroscopy has been widely used, but which often contains a lot of noise, so the spectral characteristic of the sample is seriously affected. Therefore the de-noising is very important in the spectrum analysis and processing. In the study of infrared spectroscopy, the least mean square (LMS) adaptive filter was applied in the field firstly. LMS adaptive filter algorithm can reserve the detail and envelope of the effective signal when the method was applied to infrared spectroscopy of breast cancer which signal-to-noise ratio (SNR) is lower than 10 dB, contrast and analysis the result with result of wavelet transform and ensemble empirical mode decomposition (EEMD). The three evaluation standards (SNR, root mean square error (RMSE) and the correlation coefficient (ρ)) fully proved de-noising advantages of LMS adaptive filter in infrared spectroscopy of breast cancer.

Remote sensing is one of important methods on the agricultural drought monitoring for its long-term and wide-area observations. The detection of soil moisture and vegetation growth condition are two widely used remote sensing methods on that. However, because of the time lag in the impact of water deficit on the crop growth, it is difficulty to indicate the severity of drought by once monitoring. It also cannot distinguish other negative impact on crop growth such as low temperature or solar radiation. In this paper, the joint use of soil moisture and vegetation growth condition detections was applied on the drought management during the summer of 2013 in Liaoning province, China, in which 84 counties were affected by agricultural drought. MODIS vegetation indices and land surface temperature (LST) were used to extract the drought index. Vegetation Condition Index (VCI), which only contain the change in vegetation index, and Vegetation Supply Water Index (VSWI), which combined the information of vegetation index and land surface temperature, were selected to compare the monitoring ability on drought during the drought period in Liaoning, China in 2014. It was found that VCI could be a good method on the loss assessment. VSWI has the information on the change in LST, which can indicate the spatial pattern of drought and can also be used as the early warning method in the study.

In this paper, we proposed an illumination-invariant crop extraction method based on specularity learning. Several useful contextual cues including object appearance and location inspired by recognition mechanism of human beings were introduced and integrated to machine learning architecture, generating a well-trained highlight region classifier. Combing with the Hue-intensity Look-up table and super-pixel techniques, the classifier gives the final extraction result. Comparing experiment confirmed the validity and feasibility of our method.

The demands for space debris scanning have been increasingly urgent in recent decade. The more space activities, the more urgent demands for space debris information. China has laid out space debris scanning from ground-based observation facilities. According to the latitudinal boundaries of China, north latitudes of 20deg, 30deg, 40deg, 50deg, 60deg are considered to be candidates for telescope sites. Space debris distribution is simulated under the assumption that telescopes are stationed in north latitudes of 20deg, 30deg, 40deg, 50deg, 60deg respectively. According to space debris simulations, it is recommended that the telescope dedicated to space debris scanning should be deployed in lower latitudes in order to achieve a better performance in detecting space debrises for China observing users.

We put forward a new method based on compressed encoding imaging faced with detection for height limit. In the beginning of this paper, we focus on hadamard encoding, and analysis the SNR of hadamard encoding. Then, a new encoding matrix M based on hadamard matrix been put forward. Matrix M is suitable for the sparse image obtained from the detection for height limit, and implement easily on the DMD. Compressed encoding imaging simulation based on measurement matrix M have been done in our work, and demonstrates the feasibility of the design method.

Drought risk assessment is critical to drought early-warning and drought relief decision making. Drought risk assessment is defined as the assessment on both probability and degree of damage caused by drought. This study presents a methodology for risk analysis and assessment of drought disaster t in the Poyang Lake Basin, China based on Geographical Information Systems (GIS).The main objectives of the present study are to: (1) Study the relationships among the precipitation, the water area and NDVI. (2) Assess the degree of drought disaster risk in the Poyang Lake Basin by using a method of quantitative risk analysis. The methodology employed in this study can be applied to the drought of the other area.The study introduced a new simplified approach of drought risk assessment using real-time precipitation and multi-source remote sensing data. The following conclusions are presented on the basis of study.(1)The regression shows a high level of correlation among the precipitation, drought and water area. (2)Short-time series of water area play an important role in determining if there is a drought or not or what is the degree of the drought. Further investigations are required in order to improve the precision. This can be reached by investing the relationship between the precipitation and the vegetation cover.

This paper presents a data redundancy method for distributed storage by applying Erasure code to storage system. The method involves some key technologies such as data read and written, failure detection and node redirection, and restoration algorithms. According to the theoretical analysis, this method can efficiently improve the use ratio of storage space as well as enhance reliability and availability for a storage system. Also, it can obtain the same availability of data at the cost of lower redundancy degree compared with many others storage methods. The quantitative analysis of this method’s performance is also given in the paper.

This paper carried out a study on information hiding performance using technology of histogram shift combining hybrid transform. As the approach of data selection, scan path of images is discussed. Ten paths were designed and tested on international standard testing images. Experiment results indicate that scan path has a great influence on the performance of image lossless information hiding. For selected test image, the peak of optimized path increased up to 9.84% while that of the worst path dropped 24.2%, that is to say, for different test images, scan path greatly impacts information hiding performance by influencing image redundancy and sparse matrix.

This paper presents modeling, analysis techniques and experiment of for two-Dimensional Acoustic metamaterial Structure for filtering acoustic waves. For a unit cell of an infinite two-Dimensional Acoustic metamaterial Structure, governing equations are derived using the extended Hamilton principle. The concepts of negative effective mass and stiffness and how the spring-mass-damper subsystems create a stopband are explained in detail. Numerical simulations reveal that the actual working mechanism of the proposed acoustic metamaterial structure is based on the concept of conventional mechanical vibration absorbers. It uses the incoming wave in the structure to resonate the integrated membrane-mass-damper absorbers to vibrate in their optical mode at frequencies close to but above their local resonance frequencies to create shear forces and bending moments to straighten the panel and stop the wave propagation. Moreover, a two-dimension acoustic metamaterial structure consisting of lumped mass and elastic membrane is fabricated in the lab. We do experiments on the model and The results validate the concept and show that, for two-dimension acoustic metamaterial structure do exist two vibration modes. For the wave absorption, the mass of each cell should be considered in the design. With appropriate design calculations, the proposed two-dimension acoustic metamaterial structure can be used for absorption of low-frequency waves. Hence this special structure can be used in filtering the waves, and the potential using can increase the ultrasonic imaging quality.

This paper selected 6S (second simulation of the satellite signal in the solar spectrum) model with dark pixel method to inversion aerosol optical depth by MODIS data, and got the spatial distribution and the temporal distribution of Chongqing urban area. By comparing with the sun photometer and API data, the result showed that the inversion method can be used in aerosol optical thickness monitoring in Chongqing urban area.

In the traditional digital collection, the use of FIFO and MCU is usually used. Traditional data acquisition systems are expensive and slow, When the input is a fast changing signal, the output will have a large distortion, which makes the whole system performance degradation, and not suitable for large amounts of data. In this paper, a new method of high speed data acquisition based on Blackfin DSP is presented, The analog signal is processed by the signal processing circuit, so that the amplitude of the signal is limited to the input range of the A/D converter. The whole collection system is determined by the data acquisition and control circuit. In order to further improve the speed of data transmission, DSP Blackfin uses advanced DMA technology. In the algorithm, the system is mainly used in the same sampling points for the average value of the method. Experimental results show, Using the traditional system, it will lose a lot of details, the destruction of the integrity of the signal. Using this system can well reconstruct the analog signal input, Especially in the large amount of data, it shows the incomparable advantages.

Electrical measurement system in milling balance machine currently consists of micro-controller and peripheral devices. The structure has the problems which include low integration, single signal processing algorithms and great measurement error. Therefore, electrical measurement system in milling balance machine based on embedded optimization is presented in the paper. Firstly, the device control electrical measuring system by ARM subsystem of OMAP dual-core architecture and DSP subsystem realizes digital signal processing and unbalance computing. Also, the low-pass filtering circuit is designed for solving frequency interference. Secondly, the system implement digital band-pass tracking filter based on harmonic wavelet packet. Thirdly, the system extracts any period of weak signal characteristics using the unlimited segmentation features harmonic for wavelet packet signal in the frequency domain. Simulation results show that the system effectively inhibits nearly frequency signal interference, improves signal to noise ratio, and reduces the initial imbalance signal characteristics. And test results improve that precision indexes and technical specifications could meet the design goals.

An object detection for vision-aided inventory counting is developed. The propose approach is simple to use and reduce the workload remarkably. Meanwhile, the approach count the items of interest almost in real time with an acceptable precision, which is desirable in inventory counting. The experiment results show that the proposed approach is efficient to fulfill the counting task.

In this paper we propose a novel concordance coefficients called Order Statistics Concordance Coefficients based on order statistics and Pearson's Product Moment Correlation Coefficient. For comparison, we also construct other three similar index based on Average Pearson's Product Moment Correlation Coefficient, Kendall's Concordance Coefficients, Average Kendall's tau. We propose Multivariate Normal Model to estimate the correlation coefficient, Linear Model and Nonlinear Model to model the linear and nonlinear association between multichannel signals, And we also apply the concordance coefficients to biosignal analysis developed a new organizational index for quantifying organization of AF. Statistical evidences suggest that (a) Order Statistics Concordance Coefficients have better robust than other three index; (b) capable of distinguishing fibrillatory rhythms from nonfibrillatory rhythms, such as Atiral flutter; (c) can reflect the effectiveness of adenosine, a drug commonly used during electrophysiological procedures; and (d) perform better than other three concordance coefficients.

By using optical image processing techniques, a novel text encryption and hiding method applied by double-random phase-encoding technique is proposed in the paper. The first step is that the secret message is transformed into a 2- dimension array. The higher bits of the elements in the array are used to fill with the bit stream of the secret text, while the lower bits are stored specific values. Then, the transformed array is encoded by double random phase encoding technique. Last, the encoded array is embedded on a public host image to obtain the image embedded with hidden text. The performance of the proposed technique is tested via analytical modeling and test data stream. Experimental results show that the secret text can be recovered either accurately or almost accurately, while maintaining the quality of the host image embedded with hidden data by properly selecting the method of transforming the secret text into an array and the superimposition coefficient.

Vehicle paint chip is a common evidence that plays a significant role in many criminal cases. This paper introduced a novel imaging technology - optical coherence tomography (OCT), which can realize non-invasive, rapid, high-resolution and cross-sectional imaging for the identification of vehicle paint chips. In this paper, a 1310nm swept-source OCT system was adopted to implement the paint imaging. The results show that, compared with conventional spectroscopy and SEM methods, OCT technology can directly obtain the tomographic images of the paints without slicing. In addition, the optical attenuation properties were analyzed to provide a new feature for separating different paint samples. The OCT technology and traditional methods can be combined to further narrow the scope of investigation, providing a comprehensive means to determine or rule out the suspect vehicles.

In this paper, we propose a method for single image super-resolution(SR). Given the training set produced from large amount of high-low resolution image patches, an over-complete joint dictionary is firstly learned from a pair of high-low resolution image feature space based on Restricted Boltzmann Machines (RBM). Then for each low resolution image patch densely extracted from an up-scaled low resolution input image , its high resolution image patch can be reconstructed based on sparse representation. Finally, the reconstructed image patches are overlapped to form a large image, and a high resolution image can be achieved by means of iterated residual image compensation. Experimental results verify the effectiveness of the proposed method.

Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.