This PDF file contains the front matter associated with SPIE Proceedings Volume 8514, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The huge improvements in resolution and dynamic range of current [1][2] and future CNES remote sensing missions (from 5m/2.5m in Spot5 to 70cm in Pleiades) illustrate the increasing need of efficient on-board image compressors. Many techniques have been considered by CNES during the last years in order to go beyond usual compression ratios: new image transforms or post-transforms [3][4], exceptional processing [5], selective compression [6]. However, even if significant improvements have been obtained, none of those techniques has ever contested an essential drawback in current on-board compression schemes: fixed-rate (or compression ratio). This classical assumption provides highly-predictable data volumes that simplify storage and transmission. But on the other hand, it demands to compress every image-segment (strip) of the scene within the same amount of data. Therefore, this fixed bit-rate is dimensioned on the worst case assessments to guarantee the quality requirements in all areas of the image. This is obviously not the most economical way of achieving the required image quality for every single segment. Thus, CNES has started a study to re-use existing compressors [7] in a Fixed-Quality/Variable bit-rate mode. The main idea is to compute a local complexity metric in order to assign the optimum bit-rate to comply with quality requirements. Consequently, complex areas are less compressed than simple ones, offering a better image quality for an equivalent global bit-rate. “Near-lossless bit-rate” of image segments has revealed as an efficient image complexity estimator. It links quality criteria and bit-rates through a single theoretical relationship. Compression parameters are thus automatically computed in accordance with the quality requirements. In addition, this complexity estimator could be implemented in a one-pass compression and truncation scheme.

This paper describes a SEU fault injection framework. Based on the assumption of SEU effects and SEU distribution, the quantitative analysis between measured data and simulation model is investigated. By adjusting some parameters in the simulation-based framework, the proposed framework can be very possibly close to the published data and some accelerated radiation experiments. Furthermore, how the JPEG2000 based hardware architecture is sensitive to SEUs can be found out. In terms of hardware resources and operating frequencies, some fault-tolerant techniques can be introduced to the more sensitive parts, which show the framework's effectiveness in fault-tolerant design for image compression applications.

The prediction-based lower triangular transform (PLT) features the same de-correlation and coding gain properties as the Karhunen-Loeve transform (KLT), but with a lower design and implementational cost. Unlike KLT, PLT has the perfect reconstruction property which allows its direct use for lossless compression. Our previous work has shown that PLT is good for lossless compression of ultraspectral sounder data with several thousands of channels. As the computation involves many operations on large matrices, this work will exploit the parallel compute power of graphics processing unit (GPU) to speed up the PLT encoding scheme. The CUDA (Compute Unified Device Architecture) platform by NVidia will be used for comparison with a single threaded CPU core. The experimental result reveals that our GPU implementation of the PLT encoding scheme shows a speedup of 95x compared to its original Matlab implementation on CPU. Thus it is promising to apply the GPU-based PLT encoding scheme for ultraspectral sounder data compression.

We propose a zero block detection algorithm and architecture in EBCOT. After the detailed analysis of wavelet coefficients’ precision and distribution in JPEG2000, there are three main modes of zero coefficients in the wavelet domain, i.e. zero column, zero stripe and zero code block. And we also discover that the coding information of each bit plane and the corresponding passes can be obtained simultaneously in the hardware structure. Therefore, bit plane-parallel and pass-parallel coding with zero detection is proposed, and its VLSI architecture is shown in details. The analysis and the corresponding software/hardware experimental results show that the proposed architecture reduces the processing time greatly compared with others.

A novel hardware implementation of JPEG-LS Encoder based on FPGA is introduced in this paper. Using a look-ahead technique, the critical delay paths of LOCO-I algorithm, such as feedback-loop circuit of parameters updating, are improved. Then an optimized architecture of JPEG-LS Encoder is proposed. Especially, run-mode encode process of JPEG-LS is covered in the architecture as well. Experiment results show that the circuit complexity and memory consumption of the proposed structure are much lower, while the data processing speed is much higher than some other available structures. So it is very suited for applying high-speed lossless compression of satellite sensing image onboard.

Assessment of image quality is critical for many image processing algorithms, such as image acquisition, compression, restoration, enhancement, and reproduction. In general, image quality assessment algorithms are classified into three categories: full-reference (FR), reduced-reference (RR), and no-reference (NR) algorithms. The design of NR metrics is extremely difficult and little progress has been made. FR metrics are easier to design and the majority of image quality assessment algorithms are of this type. A FR metric requires the reference image and the test image to have the same size. This may not the case in real life of image processing. In spatial resolution enhancement of hyperspectral images, such as pan-sharpening, the size of the enhanced images is larger than that of the original image. Thus, the FR metric cannot be used. A common approach in practice is to first down-sample an original image to a low resolution image, then to spatially enhance the down-sampled low resolution image using a subject enhancement technique. In this way, the original image and the enhanced image have the same size and the FR metric can be applied to them. However, this common approach can never directly assess the image quality of the spatially enhanced image that is produced directly from the original image. In this paper, a new RR metric was proposed for measuring the visual fidelity of an image with higher spatial resolution. It does not require the sizes of the reference image and the test image to be the same. The iterative back projection (IBP) technique was chosen to enhance the spatial resolution of an image. Experimental results showed that the proposed RR metrics work well for measuring the visual quality of spatial resolution enhanced hyperspectral images. They are consistent with the corresponding FR metrics.

Blind image quality assessment (QA) is a tough task especially for hyperspectral imagery which is degraded by noise, distortion, defocus, and other complex factors. Subjective hyperspectral imagery QA methods are basically measured the degradation of image from human perceptual visual quality. As the most important image quality measurement features, noise and blur, determined the image quality greatly, are employed to predict the objective hyperspectral imagery quality of each band. We demonstrate a novel no-reference hyperspectral imagery QA model based on phase congruency (PC), which is a dimensionless quantity and provides an absolute measure of the significance of feature point. First, Log Gabor wavelet is used to calculate the phase congruency of frequencies of each band image. The relationship between noise and PC can be derived from above transformation under the assumption that noise is additive. Second, PC focus measure evaluation model is proposed to evaluate blur caused by different amounts of defocus. The ratio and mean factors of edge blur level and noise is defined to assess the quality of each band image. This image QA method obtains excellent correlation with subjective image quality score without any reference. Finally, the PC information is utilized to improve the quality of some bands images.

Many modern automobiles have recently been equipped with a range of various cameras, including rear, front and side view cameras. Consequently, evaluating camera quality has become important. However, existing camera quality measurement methods standardized by ISO do not consider subjective ratings. In this paper, we propose a new perceptual video quality measurement method for automotive cameras. The proposed method measures stability, contrast and intensity metrics in text regions. Video sequences were captured with a number of automotive cameras and several subjective tests were conducted. The results show that the proposed method produced good correlations with subjective ratings.

In most applications of remote sensing data, special spatial information is required from a finer to a coarser spatial resolution with appropriate upscaling methods. The purpose of this paper is to compare and evaluate current spatial upscaling methods using MODIS remote sensing data with multiple spatial resolutions. In the research, Northeast China was selected as the study area. MODIS data with spatial resolutions of 250 m (2 bands) and 500 m (7 bands) were used as the test data. Through using the selected upscaling methods, the Band 1 and Band 2 data of MODIS were scaled up from 250 m to 500 m spatial resolution. On the basis of land cover characteristics of Northeast China, the MODIS data located in the study area was classified into the five land cover types, including water, grasslands, forests, farmlands and bare lands using maximum likelihood method. The land cover classification results were further compared with MODIS Land Cover Type product. Finally, Structural Similarity (SSIM) was selected to evaluate the effects of these upscaling methods. The research can provide more useful information for spatial scaling transformation in remote sensing data applications.

Satellite communications have been rapidly developed due to its advantageous of wide coverage and large capacity. However, an efficient and robust transmission scheme still needs carefully designed due to the time-varying and unreliable nature of links of the satellite networks. In this paper, we consider cooperative communications by selecting relays dynamically via setting an appropriate threshold according to the real-time channel conditions to avoid error propagation and then apply opportunistic network coding (ONC) to further enhance network throughput. Considering the sparse representation of natural images, block based compressive sensing (BCS) and the proposed ONC are combined in a novel way forming an energy-efficient image processing system which is easy to be implemented. Simulation results show that the scheme we proposed can gain better performances compared with traditional cooperative and network coding methods.

In this paper we present an approach to harness multi-spectral projections (MSPs) to carefully shape and locate tones in the spectrum, enabling a new and robust modulation in which a signal's discrete frequency support is used to represent symbols. This method, called Frequency Position Modulation (FPM), is an innovative extension to MT-FSK and OFDM and can be non-uniformly spread over many GHz of instantaneous bandwidth (IBW), resulting in a communications system that is difficult to intercept and jam. The FPM symbols are recovered using adaptive projections that in part employ an analog polynomial nonlinearity paired with an analog-to-digital converter (ADC) sampling at a rate at that is only a fraction of the IBW of the signal. MSPs also facilitate using commercial of-the-shelf (COTS) ADCs with uniform-sampling, standing in sharp contrast to random linear projections by random sampling, which requires a full Nyquist rate sample-and-hold. Our novel communication system concept provides an order of magnitude improvement in processing gain over conventional LPI/LPD communications (e.g., FH- or DS-CDMA) and facilitates the ability to operate in interference laden environments where conventional compressed sensing receivers would fail. We quantitatively analyze the bit error rate (BER) and processing gain (PG) for a maximum likelihood based FPM demodulator and demonstrate its performance in interference laden conditions.

Nowadays, throughputs, average path utilization and transmission efficiency are still the important factors which affect the quality of satellite communication. Recently, MORE (MAC-independence Opportunistic Routing) protocol was put forward to improve the throughput and transmission efficiency in ground wireless network. However, MORE is not appropriate for satellites network because satellites available do not have the monitor ability which is necessary in MORE and it is also too expensive to monitor in satellites. In this paper, an improved MORE protocol was proposed to apply to satellites, which is called path scalable network coding (PSNC) protocol. The theoretical analysis reveals the feasibility and effectiveness of the proposed algorithm. The simulation results show that the algorithm can effectively increase the average path utilization and the innovative packet ratio over satellite networks.

Recently, providing reliable transmission over satellite networks is still a challenging problem due to the dynamic changes of the satellite topology, the large delay and the high error rate of the satellites’ links. The benefits of network coding are well understood to solve those problems above for a large class of wireless networks. In this paper, a new coding-aware routing algorithm is proposed to decrease the large delay and improve the transmission efficiency by performing network coding opportunistically instead of using network coding always or using the shortest path routing only. The theoretical analysis and simulation results show the correctness and effectiveness of the proposed algorithm.

Mixed pixels are inevitable due to low-spatial resolutions of hyperspectral image (HSI). Linear spectrum mixture model (LSMM) is a classical mathematical model to relate the spectrum of mixing substance to corresponding individual components. The solving of LSMM, namely unmixing, is essentially a linear optimization problem with constraints, which is usually consisting of iterations implemented on decent direction and stopping criterion to terminate algorithms. Such criterion must be properly set in order to balance the accuracy and speed of solution. However, the criterion in existing algorithm is too strict, which maybe lead to convergence rate reducing. In this paper, by broaden constraints in unmixing, a new stopping rule is proposed, which can reduce rate of convergence. The experiments results prove both in runtime and iteration numbers that our method can accelerate convergence processing with only cost of little quality decrease in resulting.

Comparing with optical remote sensing techniques, passive remote sensing data have been proved to be effective for observing snowpack parameters such as snow depth and snow water equivalent, which can penetrate snowpack without clouds interferences. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3B (FY-3B) satellite is gradually used in the global environment research through November, 2011. In this paper, we proposed a snow depth retrieval algorithm to estimate snow depth in Northeast China using MWRI passive microwave remote sensing data. A decision tree method of snow identification was firstly designed to distinguish different snow cover conditions in order to eliminate other interference signals. After using the proposed decision tree method, the processing results were further used to retrieve the snow depth in Northeast China. Finally, the practical snow depth data and the MODIS data were collected for the accuracy assessment of the proposed snow depth retrieval method. The experimental results demonstrated that the RMSE of snow depth used the proposed method was approximately 3 cm in Northeast China.

In Hyperspectral imaging the sensors measure the light refelcted by the earth surface in differents wavelenghts, usually the number of measures is between one and several hundreds per pixel. This generates huge data ammounts that must be transmitted to the earth and for subsequent processing. The real-time requirements of some applications make that the bandwidth required between the sensor and the earth station is very large. The Compressive Sensing (CS) framework tries to solve this problem. Althougth the hyperspectral images have thousands of bands usually most of the bands are highly correlated. The CS exploit this feature of the hyperspectral images and allow to represent most of the information in few bands instead of hundreds. This compressed version of the data can be sent to a earth station that will recover the original image using the corresponding algorithm. In this paper we describe an Compressive Sensing algorithm called Hyperspectral Coded Aperture (HYCA) that was developed in previous works. This algorithm has a parameter that need to be optimized empirically in order to get the better results. In this work we present a novel way to reconstruct the compressed images under the HYCA framework in which we do not need to optimize any parameter due to all parameters can be estimated automatically. The results show that this new way to reconstruct the images without the parameter provides similar results with respect to the best parameter setting for the old algorithm. The proposed approach have been tested using synthetic data and also we have used the dataset obtained by the AVIRIS sensor of NJPL over the Cuprite mining district in Nevada.

Neuroscience-inspired machine vision algorithms are of current interest in the areas of detection and monitoring of climate change impacts, and general Land Use/Land Cover classification using satellite image data. We describe an approach for automatic classification of land cover in multispectral satellite imagery of the Arctic using sparse representations over learned dictionaries. We demonstrate our method using DigitalGlobe Worldview-2 8-band visible/near infrared high spatial resolution imagery of the MacKenzie River basin. We use an on-line batch Hebbian learning rule to build spectral-textural dictionaries that are adapted to this multispectral data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. We explore unsupervised clustering in the sparse representation space to produce land-cover category labels. This approach combines spectral and spatial textural characteristics to detect geologic, vegetative, and hydrologic features. We compare our technique to standard remote sensing algorithms. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.

In this paper, we develop a new web-based system for unsupervised classification of satellite images available from the Google Maps engine. The system has been developed using the Google Maps API and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the desired zoom level). For this purpose, we use a processing chain made up of the well-known ISODATA and k-means algorithms, followed by spatial post-processing based on majority voting. The system is currently hosted on a high performance server which performs the execution of classification algorithms and returns the obtained classification results in a very efficient way. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package. The server has access to a cluster of commodity graphics processing units (GPUs), hence in future work we plan to perform the processing in parallel by taking advantage of the cluster.

In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage. The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe County. Experimental results have demonstrated that NFL embedding method is an effective transformation for dimension reduction in land cover classification of earth remote sensing.

JPEG-LS is an ISO/ITU lossless/near-lossless compression standard for continuous-tone images with both low complexity and good performance. However, the lack of rate control in JPEG-LS makes it unsuitable for applications, which have the requirement of the compression to a pre-specified size for purpose of effective storage management or effective bandwidth management. This paper proposes an efficient rate control scheme for JPEG-LS with a high bitrate. It is based on a good relationship for the optimal quantization steps of different slices and a good relationship for the optimal target bitrates of different slices. Comparing with the most previous JPEG-LS with rate control schemes, the proposed rate control scheme has a uniform performance for the whole image, and it is more suited for the near-lossless compression, but the most previous JPEG-LS with rate control schemes have the non-uniform performance. The experimental results show that the proposed rate control scheme achieves better compression performance for remote sensing compression with a high bitrate.

In this paper, we investigate the frequency sensitivity of the human visual system. The human visual system reacts differently at different frequencies. Based on this observation, we used different quantization steps for different frequency components to explore the possibility of improving coding efficiency while maintaining perceptual video quality. In other words, small quantization steps were used for sensitive frequency components while large quantization steps were used for less sensitive frequency components.

Vector quantization is an optimal compression strategy for hyperspectral imagery, but it can’t satisfy the fixed bitrate application. In this paper, we propose a vector quantization algorithm for AVIRIS hyperspectral imagery compression with fixed low bitrate. The 2D-TCE lossless compression for codebook image and index image, the codebook reordering, the remove water absorbed band algorithm are introduced to the classical vector quantization, and the bitrate distribution is replaced by choosing the appropriate codebook size algorithm. Experimental results show that the proposed vector quantization has a better performance than the traditional hyperspectral imagery lossy compression with fixed low bitrate.

This paper presents a reversible fragile data hiding scheme for tampering detection in remote sensing images based on the histogram shifting approach. The image to be protected is divided into blocks of a reduced size and a subset of the image bands are selected for embedding. Instead of using the histogram of each separate band, the shifting process is applied to the histogram of the maximum component (or infinity norm) of the vectors obtained with the selected bands. The proposed approach is reversible and thus, the original image can be fully recovered once it has been authenticated. The method is designed to detect specific forged blocks (areas) of the protected image and is shown to succeed to detect copy and replace attacks. In addition, the experimental results, presented for the Cuprite AVIRIS image, show that the method yields extremely high transparency, with PSNR larger than 100 dB prior to reversing the scheme and recovering the original image.

In this paper, we develop a new framework for spectral unmixing of multispectral remote sensing images with limited spectral resolution. Our proposed approach performs dimensionality expansion by taking advantage of the spatial information contained in such images. For this purpose, in this work, we experiment with morphological profiles and morphological attribute filters, which allow expanding the dimensionality of the original image and obtaining a detailed signature (profile) at each pixel using the SVM classifier. This allows for the application of spectral unmixing techniques that integrate both the spatial and the spectral information, since the unmixing is not only based on the original multispectral/color information but also takes into account the additional bands included by exploiting the spatial information. The unmixing chain considered in this work comprises a classic endmember extraction algorithm: vertex component analysis (VCA) followed by fully constrained linear spectral unmixing (FCLSU) to estimate the abundance of each endmember in each pixel of the image. Kernel principal component analysis (KPCA) is also used in the considered chain, to increase dimensionality in the spectral domain only and to perform feature extraction. In order to quantitatively validate the proposed framework, we use the RGB bands of a set of registered hyperspectral images. Specifically, we use the ground-truth to validate the unmixing results obtained for the lower spatial resolution scenes. Our experimental results indicate that the proposed dimensionality expansion strategy allows for the successful unmixing of multispectral satellite images, specially for RGB/color images.

Remote sensing technology offers a very useful tool for understanding the earth. It brings us mass of information on the earth and makes the view of human being broaden greatly. With the remote sensing technology developing, the spatial resolution of image has been greatly upgraded. TIFF file format is a common format in graphics image processing. GeoTIFF format is extended from TIFF. The GeoTIFF is widely used as a storage format of remote sensing. But the format of TIFF and GeoTIFF is complex and they can’t be used efficiently under the windows operating system. In this paper, an efficient file reading platform for high-resolution remote sensing image is proposed. This new design builds an efficient reading platform of TIFF and GeoTIFF format file by image tiling and adding a cache in the middle of the intermediate file. At the same time this design is a part of high resolution remote sensing image compression system. It will have important theoretical and practical value for compression of the remote sensing images in the future.

Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium or long distance by an airbone or satellite sensor. Over the last few years, hyperspectral image data sets have been collected for a great amount of locations over the world, using a variety of instruments for Earth observation. Despite the increasing importance of hyperspectral images in remote sensing applications, there is no common repository of hyperspectral data intended to distribute and share hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available for public use are spread among different storage locations and present significant heterogeneity regarding the storage format, associated meta-data (if any), or ground-truth availability. As a result, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we take a necessary first step towards the development of a digital repository for remotely sensed hyperspectral data. The proposed system allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results, with the ultimate goal of sharing publicly available hyperspectral images within the remote sensing community. The database has been designed in order to allow storing relevant information for the hyperspectral data available through the system, including basic image characteristics (width, height, number of bands, format) and more advanced meta-data (ground-truth information, publications in which the data has been used). The current implementation consists of a front-end to ease the management of images through a web interface, thus containing both synthetic and real hyperspectral images from two highly representative instruments, such as NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining District in Nevada. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images on the spectral unmixing information (spectrally pure components or endmembers and their associated abundances in the scene). This information is stored as meta-data associated to each hyperspectral image instance, and then used to search and retrieve images based on information content. This paper presents the design of the system and a preliminary validation of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images stored in the database.