On May 12 in 2008, a Magnitude 8.0 earthquake hit Wenchuan in China, and the casualty shocked the whole world. The
landslide was a frequent secondary disaster in this earthquake, so to analyze the mechanism of landslides in the disaster
area is very important for post-earthquake reconstruction. The study area is located in PingWu County, which was also
hit by the earthquake severely. And the data sources are ETM+ image, DEM and interpreted ALOS image. This paper
considered four potential driving factors for landslides, and they are land cover, lineament, slope and drainage. The land
cover was classified based on the density of vegetation, and sub-pixel analysis was employed; Density of lineament was
calculated by Sobel operator and image segmentation; Slope was classified by using a threshold; Drainage was
considered without numerical analysis, because it is significant and simple in study area. To find out how they influenced
the landslides, conditional probability was utilized as a measurement. The result shows that areas in sparse vegetation,
dense lineament and steep topography were easy to meet landslides, while drainages also induced landslides.
In this paper, a new texture segmentation approach based on Markov random field (MRF) and global optimal method of
particle swarm optimization (PSO) is proposed. According to this approach, firstly the MRF texture model is established,
and potential function of Gibbs distribution and the calculating method of Gibbs parameters are represented. Then the
fitness function is designed and the PSO is adopted here to solve the maximum a posterior (MAP) estimate. Finally, a
comparison of the new algorithm with the Metropolis algorithm and the Gibbs Sampler is made in texture segmentation
of remote sensing images. Results show that PSO algorithm can reduce the computational complexity and is much more
An efficient edge detection for remote sensing image based on Wold-like decomposition in random field is presented in
this paper. In such assumption that the image field is a realization of a 2-D homogeneous random field, image can be
decomposed into a sum of two mutually orthogonal, spatially homogeneous components, namely deterministic in the
prediction theory sense and purely indeterministic in the prediction theory. The Wold decomposition can be described by
"periodicity," "directionality," and "randomness," approximating what is indicated to be the three most important
dimensions of human perception. So, the remote sensing images are firstly decomposed into two components:
deterministic component and indeterministic component. On the basis of Wold-like decomposition a new approach of
low image processing, SUSAN algorithm, is estimated and recommended in the edge detection on the periodicity
component, which presents the structural information convenient for detecting edge. Then this paper made some
improvement of the approach in edge detection. The experiments show that the results of edge detection through Wold
decomposition are better than that of no Wold decomposition. Simultaneously, the Wold texture modal is applicable to a
wide variety of texture types, from structural to stochastic texture. And this modal gives a unified, perfect description of
texture in natural images.
Remote sensing image classification is an important and complex problem. Conventional remote sensing image classification methods are mostly based on Bayes' subjective probability theory. Because there are many defects on solving uncertainty problem, new tendency is that mathematical theory of evidence is applied to remote sensing image classification. At first, this paper introduces differences between Dempster-Shafer's(D-S) evidence theory and Bayes' subjective probability theory in solving uncertainty problem, main definitions and algorithms of D-S evidence theory. Especially degree of belief, degree of plausibility and degree of support are the bridges that D-S evidence theory is used in other fields. It emphatically introduced Support function that D-S evidence theory is used on pattern recognition, and degree of support is applied to classification. We acquire degree of support surfaces according to large classes, such as urban land, farmland, forest land, and water, then use "hard classification" to gain initial classification result. If initial classification accuracy is unfitted to acquirement, do reclassification for degree of support surfaces of less than threshold until final classification result reaches satisfying accuracy. We conclude that main advantages of this method are that it can go on reclassification after classification and its classification accuracy is very high. This method has dependable theory, intensive application, easy operation and research potential.
In order to hide secrete information in remote sensing image, we proposed an algorithm for secrete information hiding which was adaptive to the feature of remote sensing image. Firstly, we segmented and extracted the secrete information in remote sensing image, and made supplement of gray values in the area corresponding with the secrete information and then produced the disguised remote sensing image which was wiped off secrete information. Then we used for reference the idea of digital watermarks and feature of HVS (Human Visual System) and embedded the secrete sub-image imperceptibly and adaptively into the disguised remote sensing image to produce the disguised remote sensing image in which there hid secrete sub-image. In addition, during the course of extracting secrete information and resuming the remote sensing image, this algorithm didn’t need the original remote sensing image and was a blind one. To those algorithms for information hiding, imperceptivity and amount of hidden information are the most important and robustness is less. And experimental results show that this algorithm is not only quite transparent and has a good effect for large amount of secrete information hiding, but also has a strong robustness against such image attacks as JPEG lossy compression, median filtering, noise adding, scaling, cropping and rotation. Furthermore this algorithm has no influence on such applications as edge detection and image classification of the disguised remote sensing image which has been hidden the secrete information.
In this article, we proposed an effective adaptive 2-dimension blind watermarking algorithm based on feature of a remote sensing image. This algorithm exploited a gray image as the watermark, pretreated the watermark image by Arnold confusion and wavelet compression, and embedded it into the selected subband of wavelet transformation domain of the remote sensing image according to neighboring symbol's mean value and odd-even adjugement rule, moreover, detected watermarks without the original remote sensing image. The attack analysis and experimental results show that the watermarking algorithm is transparent and robust, with accurate watermark detecting results and low complexity, and it also has strong robustness against various image attacks such as JPEG lossy compression, median filtering, additive noise, scaling, cropping, rotation, random geometrical attack and Stirmark attack. Furthermore, after embedding watermarks, there is almost no influence on such applications of the remote sensing image as edge detection and image classification.
The paper researched the theory of concept lattice and the algorithms of association rule mining based on concept lattice, introduced the methods into remote sensing image mining, analyzed and discussed the spectrum characteristics mining, texture characteristics mining, shape characteristics mining and spatial distributing laws mining, analyzed the application of remote sensing image mining, such as the automation classification, intelligent retrieval of remote sensing image etc., finally, discussed some research directions.
The central idea of this paper is that image data mining could be performed directly on the 2D image representation, by applying some scan techniques on the 2D image, which are different than the raster scan. In this paper, we present the comparison of spatial data sets using bit sequential format on a unique vector form which converts between one quadrant tree and some sub-quadrant trees. Then, we describe how the bit-vector might be used to generate the associations among scan patterns in which when some object attributes are extracted in a data process, the others are extracted too.
KEYWORDS: Information fusion, Image segmentation, Image processing, Remote sensing, Computer programming, Geographic information systems, Data processing, Raster graphics, Data conversion, Data analysis
The primary combining of remote sensing and GIS is mainly realized by the transforms of data structure. Because of its own limitations, it is in urgent need to investigate the integration of RS and GIS in higher levels. In this paper, we have discussed the different types of combinings between RS and GIS, and proposed that GIS data should be directly brought into image processing from the first. A tentative idea of how to use the method of granularity to study the common processing unit of RS and GIS is described. Some man-made objects and green lands are chosen for their relative importance. The method called (lambda) - f(alpha) _**p representation is presented here for image compositing based on the concepts of connection cost. The example for the determination of granularity of spatial data processing relating to run-length-code line is also given.
Now the developing research of Agent can help operators to do the routine assignments, by which we can economize the precious resources and improve the real-time image analysis of the computers. This paper firstly makes a brief introduction of the Agent conception. Then we make some discussions about the multispectral images of a certain area, which is based on the concept of Agent. The main objects of this paper are inspections of forest (grassland) fire. The purpose of this paper is to propose three stages with which Agent could monitor the wildly areas and make decision automatically, without operators' intervention. First stage, if the value of pixels are more than a given threshold, Agent will give the operators an alarm and notify the operators that there are something happened; Second stage, analyze data and self-learning; Third stage, according to the database and knowledge database, Agents make decisions. As the decisions will be influenced by many factors, so some models, such as heat sources model, weather model, fire model, vegetation model are needed.
The typed association of objects for image analysis via structured quotient set is presented in this paper. From a landscape point of view, the elements of spatial structure are organized into some distinct patches, which are the smallest homogeneous units of landscape at the spatial scale we can see. The main advantage of such patches is that it gives us the typed association of objects. In order to remark the relationship between the components of an image corresponding to the patch, the notion of diffusion- concentration is introduced, whose practical meaning in the patch is to join a pixel to one another and determine the weights of the links. Another research goal is to explore the properties of variogram function, from which the weights of neurons for growing collection of the typed association of objects by quotient set can be derived.
Imaging spectrometer is one important aspect of contemporary remote sensing developments. It is characterized by huge amount of supermultipal spectral image data provision. For less remote sensing image data provision, we used to put all the data directly into the process of image object recognition without making any data fusion. Even occasional data fusion is experiential. This situation is not suitable for imaging spectrometer. The supermultipal bands of imaging spectrometer provide us with much more freedom on available choices of suitable images for procession and information extraction; at the same time, fusion between different related image data aimed at themes extracting can reduce the amount of data on one hand, and make most use of imaging spectrometer image data on the other. And to some extent, image data fusion is of utmost importance and practical significance for theme information extracting from imaging spectrometer images. This paper discusses fusion methods for conventional remote sensing data. Based on analysis of imaging spectrometer, it proposes method for imaging spectrometer image data.