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

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

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

Diseño, implementación y optimización del sistema de compresión de imágenes sobre el ordenador de a bordo del proyecto de nanosátelite Eye-Sat

Eye-Sat es un Proyecto de nano satélites, dirigido por el CNES (Centre National d’Etudes Spatiales) y desarrollado principalmente por estudiantes de varias escuelas de ingeniería del territorio francés. El objetivo de este pequeño telescopio no solo radica en la oportunidad de realizar la demostración de distintos dispositivos tecnológicos, sino que también tiene como misión la adquisición de fotografías en la bandas de color e infrarrojo de la vía Láctea, así como el estudio de la intensidad y polarización de la luz Zodiacal. Los requerimientos de la misión exigen el desarrollo de un algoritmo de compresión de imágenes sin pérdidas para las imágenes “Color Filter Array” CFA (Bayer) e infrarrojas adquiridas por el satélite. Como miembro de la comisión consultativa para los sistemas espaciales, CNES ha seleccionado el estándar CCSDS-123.0-B como algoritmo base para cumplir los requerimientos de la misión. A este algoritmo se le añadirán modificaciones o mejoras, adaptadas a las imágenes tipo, con el fin de mejorar las prestaciones de compresión y de complejidad. La implementación y la optimización del algoritmo será desarrollada sobre la plataforma Xilinx Zynq® All Programmable SoC, el cual incluye una FPGA y un Dual-core ARM® Cortex™-A9 processor with NEONTM DSP/FPU Engine

Zooplankton visualization system: design and real-time lossless image compression

In this thesis, I present a design of a small, self-contained, underwater plankton imaging system. I base the imaging system’s design on an embedded PC architecture based on PC/104-Plus standards to meet the compact size and low power requirements. I developed a simple graphical user interface to run on a real-time operating system to control the imaging system. I also address how a real-time image compression scheme implemented on an FPGA chip speeds up image transfer speeds of the imaging system. Since lossless compression of the image is required in order to retain all image details, I began with an established compression scheme like SPIHT, and latter proposed a new compression scheme that suits the imaging system’s requirements. I provide an estimate of the total amount of resources required and propose suitable FPGA chips to implement the compression scheme. Finally, I present various parallel designs by which the FPGA chip can be integrated into the imaging system

Isorange pairwise orthogonal transform

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The Pairwise Orthogonal Transform (POT) is one such transform that has been specifically devised for resource-constrained contexts like those found on board satellites or airborne sensors. Combining the POT with a 2D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion -i.e., the increase in bit depth of transformed images- is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2D standard CCSDS 122.0-B-1. This paper introduces the Isorange Pairwise Orthogonal Transform, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance

Lossless compression of hyperspectral images

Band ordering and the prediction scheme are the two major aspects of hyperspectral imaging which have been studied to improve the performance of the compression system. In the prediction module, we propose spatio-spectral prediction methods. Two non-linear spectral prediction methods have been proposed in this thesis. NPHI (Non-linear Prediction for Hyperspectral Images) is based on a band look-ahead technique wherein a reference band is included in the prediction of pixels in the current band. The prediction technique estimates the variation between the contexts of the two bands to modify the weights computed in the reference band to predict the pixels in the current band. EPHI (Edge-based Prediction for Hyperspectral Images) is the modified NPHI technique wherein an edge-based analysis is used to classify the pixels into edges and non-edges in order to perform the prediction of the pixel in the current band. Three ordering methods have been proposed in this thesis. The first ordering method computes the local and global features in each band to group the bands. The bands in each group are ordered by estimating the compression ratios achieved between the entire band in the group and then ordering them using Kruskal\u27s algorithm. The other two methods of ordering compute the compression ratios between b-neighbors in performing the band ordering

Techniques of design optimisation for algorithms implemented in software

The overarching objective of this thesis was to develop tools for parallelising, optimising, and implementing algorithms on parallel architectures, in particular General Purpose Graphics Processors (GPGPUs). Two projects were chosen from different application areas in which GPGPUs are used: a defence application involving image compression, and a modelling application in bioinformatics (computational immunology). Each project had its own specific objectives, as well as supporting the overall research goal. The defence / image compression project was carried out in collaboration with the Jet Propulsion Laboratories. The specific questions were: to what extent an algorithm designed for bit-serial for the lossless compression of hyperspectral images on-board unmanned vehicles (UAVs) in hardware could be parallelised, whether GPGPUs could be used to implement that algorithm, and whether a software implementation with or without GPGPU acceleration could match the throughput of a dedicated hardware (FPGA) implementation. The dependencies within the algorithm were analysed, and the algorithm parallelised. The algorithm was implemented in software for GPGPU, and optimised. During the optimisation process, profiling revealed less than optimal device utilisation, but no further optimisations resulted in an improvement in speed. The design had hit a local-maximum of performance. Analysis of the arithmetic intensity and data-flow exposed flaws in the standard optimisation metric of kernel occupancy used for GPU optimisation. Redesigning the implementation with revised criteria (fused kernels, lower occupancy, and greater data locality) led to a new implementation with 10x higher throughput. GPGPUs were shown to be viable for on-board implementation of the CCSDS lossless hyperspectral image compression algorithm, exceeding the performance of the hardware reference implementation, and providing sufficient throughput for the next generation of image sensor as well. The second project was carried out in collaboration with biologists at the University of Arizona and involved modelling a complex biological system – VDJ recombination involved in the formation of T-cell receptors (TCRs). Generation of immune receptors (T cell receptor and antibodies) by VDJ recombination is an enormously complex process, which can theoretically synthesize greater than 1018 variants. Originally thought to be a random process, the underlying mechanisms clearly have a non-random nature that preferentially creates a small subset of immune receptors in many individuals. Understanding this bias is a longstanding problem in the field of immunology. Modelling the process of VDJ recombination to determine the number of ways each immune receptor can be synthesized, previously thought to be untenable, is a key first step in determining how this special population is made. The computational tools developed in this thesis have allowed immunologists for the first time to comprehensively test and invalidate a longstanding theory (convergent recombination) for how this special population is created, while generating the data needed to develop novel hypothesis