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

Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images

Predictive coding is attractive for compression of hyperspecral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recommendation. The proposed rate algorithm overhauls our previous scheme by being orders of magnitude faster and simpler to implement, while still providing the same accuracy in terms of output rate and comparable or better image quality

Compression algorithm for Multi Element Telescope for Imaging and Spectroscopy (METIS)

The compression algorithm defined for METIS (Multi Element Telescope for Imaging and Spectroscopy) arises from the standard CCSDS 123.0-r-1, that has been modified and adapted to the mission purposes, and integrated with other pieces of software to let the compressor work in the most efficient way with the expected acquisitions of the sensor. The major modification is the insertion in the prediction loop of a uniform scalar quantizer, extending the standard to a near-lossless version; in addition a local decoder has been added as well, in order to keep a local copy of the dequantized residuals to allow a correct reconstruction at the decoder side. A lossy compression can even be executed in a variable-quality way, meaning that it is possible to change the quantization step size among successive image lines. The ability of the original software to process three-dimensional images has been kept but adapted to the mission needs: instead of considering wavelength, consecutive acquisitions are collected together to build up the 3D cube, so that time becomes the third dimension; and since solar acquisitions change really slowly in time, the effectiveness of this adjustment works very well and prediction of the current pixels becomes much more accurate if considering the previous acquisitions ones. Further, a pre-processing routine has been developed to exploit the geometry of the images; it consists in a re-mapping of the pixels in order to take advantage of the radial structure of solar acquisitions, through a function that has been named “radialization”. It receives the standard image as input, and computes for every pixel the distance and the angle with respect to the center; these become the two new coordinates, as it happens when switching from a Cartesian system to a polar one. The triangular-shaped output is then centered and padded in order to keep a rectangular structure, and matrices for the two dimensions are kept, so that the whole piece of code can be executed only once, and the “radialized” image can be then obtained by a simple mapping using these structures, resulting in a really light operation from a computational point of view; a further advantage can be identified in the lack of interpolation among pixels, so that eventually, the compression of the image, or better of a section of it, can occur losslessly. Radialization also simplifies a possible selection of areas of interest of the image: for example it would be possible to keep the nearest solar corona area coded losslessly, and decreasing linearly the quality of the reconstruction in a radial sense by successive circular corona-shaped structures, by using variable lossy compression for consecutive radialized image lines

The CCSDS 123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard: A comprehensive review

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, “Low- Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compression, while maintaining backward compatibility. The main novelty of the new issue is support for near-lossless compression, i.e., lossy compression with user-defined absolute and/or relative error limits in the reconstructed images. This new feature is achieved via closed-loop quantization of prediction errors. Two further additions arise from the new near lossless support: first, the calculation of predicted sample values using sample representatives that may not be equal to the reconstructed sample values, and, second, a new hybrid entropy coder designed to provide enhanced compression performance for low-entropy data, prevalent when non lossless compression is used. These new features enable significantly smaller compressed data volumes than those achievable with CCSDS 123.0-B-1 while controlling the quality of the decompressed images. As a result, larger amounts of valuable information can be retrieved given a set of bandwidth and energy consumption constraints

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

The new CCSDS standard for low-complexity lossless and near-lossless multispectral and hyperspectral image compression

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data

The new CCSDS standard for low-complexity lossless and near-lossless multispectral and hyperspectral image compression

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data

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