Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems

A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm

Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data

Iterative enhanced multivariance products representation for effective compression of hyperspectral images.

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods

Lossless compression of satellite multispectral and hyperspectral images

En esta tesis se presentan nuevas técnicas de compresión sin pérdida tendientes a reducir el espacio de almacenamiento requerido por imágenes satelitales. Dos tipos principales de imágenes son tratadas: multiespectrales e hiperespectrales. En el caso de imágenes multiespectrales, se desarrolló un compresor no lineal que explota tanto las correlaciones intra como interbanda presentes en la imagen. Este se basa en la transformada wavelet de enteros a enteros y se aplica sobre bloques no solapados de la imagen. Diferentes modelos para las dependencias estadísticas de los coeficientes de detalle de la transformada wavelet son propuestos y analizados. Aquellos coeficientes que se encuentran en las subbandas de detalle fino de la transformada son modelados como una combinación afín de coeficientes vecinos y coeficientes en bandas adyacentes, sujetos a que se encuentren en la misma clase. Este modelo se utiliza para generar predicciones de otros coficientes que ya fueron codificados. La información de clase se genera mediante la cuantización LloydMax, la cual también se utiliza para predecir y como contextos de condicionamiento para codificar los errores de predicción con un codificador aritmético adaptativo. Dado que el ordenamiento de las bandas también afecta la precisión de las predicciones, un nuevo mecanismo de ordenamiento es propuesto basado en los coeficientes de detalle de los últimos dos niveles de la transformada wavelet. Los resultados obtenidos superan a los de otros compresores 2D sin pérdida como PNG, JPEG-LS, SPIHT y JPEG2000, como también a otros compresores 3D como SLSQ-OPT, JPEG-LS diferencial y JPEG2000 para imágenes a color y 3D-SPIHT. El método propuesto provee acceso aleatorio a partes de la imagen, y puede aplicarse para la compresión sin pérdida de otros datos volumétricos. Para las imágenes hiperespectrales, algoritmos como LUT o LAIS-LUT que revisten el estado del arte para la compresión sin pérdida para este tipo de imágenes, explotan la alta correlación espectral de estas imágenes y utilizan tablas de lookup para generar predicciones. A pesar de ello, existen casos donde las predicciones no son buenas. En esta tesis, se propone una modificación a estos algoritmos de lookup permitiendo diferentes niveles de confianza a las tablas de lookup en base a las variaciones locales del factor de escala. Los resultados obtenidos son altamente satisfactorios y mejores a los de LUT y LAIS-LUT. Se han diseñado dos compresores sin pérdida para dos tipos de imágenes satelitales, las cuales tienen distintas propiedades, a saber, diferente resolución espectral, espacial y radiométrica, y también de diferentes correlaciones espectrales y espaciales. En cada caso, el compresor explota estas propiedades para incrementar las tasas de compresión.In this thesis, new lossless compression techniques aiming at reducing the size of storage of satellite images are presented. Two type of images are considered: multispectral and hyperspectral. For multispectral images, a nonlinear lossless compressor that exploits both intraband and interband correlations is developed. The compressor is based on a wavelet transform that maps integers into integers, applied to tiles of the image. Different models for statistical dependencies of wavelet detail coefficients are proposed and analyzed. Wavelet coefficients belonging to the fine detail subbands are successfully modelled as an affine combination of neighboring coefficients and the coefficient at the same location in the previous band, as long as all these coefficients belong to the same landscape. This model is used to predict wavelet coefficients by means of already coded coefficients. Lloyd-Max quantization is used to extract class information, which is used in the prediction and later used as a conditioning context to encode prediction errors with an adaptive arithmetic coder. The band order affects the accuracy of predictions: a new mechanism is proposed for ordering the bands, based on the wavelet detail coefficients of the 2 finest levels. The results obtained outperform 2D lossless compressors such as PNG, JPEG-LS, SPIHT and JPEG2000 and other 3D lossless compressors such as SLSQ-OPT, differential JPEG-LS, JPEG2000 for color images and 3D-SPIHT. Our method has random access capability, and can be applied for lossless compression of other kinds of volumetric data. For hyperspectral images, state-of-the-art algorithms LUT and LAIS-LUT proposed for lossless compression, exploit high spectral correlations in these images, and use lookup tables to perform predictions. However, there are cases where their predictions are not accurate. In this thesis a modification based also on look-up tables is proposed, giving these tables different degrees of confidence, based on the local variations of the scaling factor. Our results are highly satisfactory and outperform both LUT and LAIS-LUT methods. Two lossless compressors have been designed for two different kinds of satellite images having different properties, namely, different spectral resolution, spatial resolution, and bitdepth, as well as different spectral and spatial correlations. In each case, the compressor exploits these properties to increase compression ratios.Fil:Acevedo, Daniel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Compresión sin pérdida de imágenes satelitales multiespectrales e hiperespectrales

En esta tesis se presentan nuevas técnicas de compresión sin pérdida tendientes a reducir el espacio de almacenamiento requerido por imágenes satelitales. Dos tipos principales de imágenes son tratadas: multiespectrales e hiperespectrales. En el caso de imágenes multiespectrales, se desarrolló un compresor no lineal que explota tanto las correlaciones intra como interbanda presentes en la imagen. Este se basa en la transformada wavelet de enteros a enteros y se aplica sobre bloques no solapados de la imagen. Diferentes modelos para las dependencias estadísticas de los coeficientes de detalle de la transformada wavelet son propuestos y analizados. Aquellos coeficientes que se encuentran en las subbandas de detalle fino de la transformada son modelados como una combinación afín de coeficientes vecinos y coeficientes en bandas adyacentes, sujetos a que se encuentren en la misma clase. Este modelo se utiliza para generar predicciones de otros coficientes que ya fueron codificados. La información de clase se genera mediante la cuantización LloydMax, la cual también se utiliza para predecir y como contextos de condicionamiento para codificar los errores de predicción con un codificador aritmético adaptativo. Dado que el ordenamiento de las bandas también afecta la precisión de las predicciones, un nuevo mecanismo de ordenamiento es propuesto basado en los coeficientes de detalle de los últimos dos niveles de la transformada wavelet. Los resultados obtenidos superan a los de otros compresores 2D sin pérdida como PNG, JPEG-LS, SPIHT y JPEG2000, como también a otros compresores 3D como SLSQ-OPT, JPEG-LS diferencial y JPEG2000 para imágenes a color y 3D-SPIHT. El método propuesto provee acceso aleatorio a partes de la imagen, y puede aplicarse para la compresión sin pérdida de otros datos volumétricos. Para las imágenes hiperespectrales, algoritmos como LUT o LAIS-LUT que revisten el estado del arte para la compresión sin pérdida para este tipo de imágenes, explotan la alta correlación espectral de estas imágenes y utilizan tablas de lookup para generar predicciones. A pesar de ello, existen casos donde las predicciones no son buenas. En esta tesis, se propone una modificación a estos algoritmos de lookup permitiendo diferentes niveles de confianza a las tablas de lookup en base a las variaciones locales del factor de escala. Los resultados obtenidos son altamente satisfactorios y mejores a los de LUT y LAIS-LUT. Se han diseñado dos compresores sin pérdida para dos tipos de imágenes satelitales, las cuales tienen distintas propiedades, a saber, diferente resolución espectral, espacial y radiométrica, y también de diferentes correlaciones espectrales y espaciales. En cada caso, el compresor explota estas propiedades para incrementar las tasas de compresión