Data Compression for Helioseismology

Die effiziente Kompression von Daten wird eine wichtige Rolle für mehrere bevorste- hende und geplante Weltraummissionen spielen, die Helioseismologie betreiben werden, wie beispielsweise Solar Orbiter. Solar Orbiter ist die nächste Mission, die Helioseismologie beinhaltet, und soll im Oktober 2018 gestartet werden. Das Hauptmerkmal von Solar Orbiter ist der Orbit. Die Umlaufbahn des Satelliten wird zur Ekliptik geneigt sein, sodass der Satellit einen solaren Breitengrad von bis zu 33 Grad erreichen wird. Dies wird erstmals ermöglichen, die Pole der Sonne mit Hilfe von lokaler Helioseismologie zu studieren. Zusätzlich dazu können kombinierte Beobachtungen von Solar Orbiter und einem anderen Instrument dazu benutzt werden, die tiefen Schichten der Sonne mittels stereoskopischer Helioseismologie zu erforschen. Die Aufnahmen der Dopplergeschwindigkeit und der Kontinuumsintensität, die für Helioseismologie benötigt werden, werden vom Polarimetric and Helioseismic Imager (PHI) geliefert werden. Große Hindernisse für Helioseismologie mit Solar Orbiter sind die niedrige Datenüber- tragungsrate und die (wahrscheinlich) kurzen Beobachtungszeiten. Außerdem erfordert die Untersuchung der Pole der Sonne Beobachtungen in der Nähe des Sonnenrandes, sogar von dem geneigten Orbit von Solar Orbiter aus. Dies kann zu systematischen Fehlern führen. In dieser Doktorarbeit gebe ich eine erste Einschätzung ab, wie stark Helioseismologie von verlustbehafteter Datenkompression beeinflusst wird. Mein Schwerpunkt liegt dabei auf der Solar Orbiter Mission, die von mir erzielten Ergebnisse sind aber auch auf andere geplante Missionen übertragbar. Zunächst habe ich mit Hilfe synthetischer Daten die Eignung des PHI Instruments für Helioseismologie getestet. Diese basieren auf Simulationen der Konvektion nahe der Sonnenoberfläche und einem Modell von PHI. Ich habe eine sechs Stunden lange Zeitreihe synthetischer Daten erstellt, die die gleichen Eigenschaften wie die von PHI erwarteten Daten haben. Hierbei habe ich mich auf den Einfluss der Punktspreizfunktion, der Vibrationen des Satelliten und des Photonenrauschen konzentriert. Die von diesen Daten abgeleitete spektrale Leistungsdichte der solaren Oszillationen legt nahe, dass PHI für Helioseismologie geeignet sein wird. Aufgrund der niedrigen Datenübertragungsrate von Solar Orbiter müssen die von PHI für die Helioseismologie gewonnenen Daten stark komprimiert werden. Ich habe den Einfluss von Kompression mit Hilfe von Daten getestet, die vom Helioseismic and Magnetic Imager (HMI) stammen. HMI ist ein Instrument an Bord des Solar Dynam- ics Observatory Satelliten (SDO), der 2010 gestartet worden ist. HMI erstellt mit hoher zeitlicher Abfolge Karten der Kontinuumsintensität, der Dopplergeschwindigkeit und des kompletten Magnetfeldvektors für die komplette von der Erde aus sichtbare Hemispäre der Sonne. Mit Hilfe mit von HMI aufgenommenen Karten der Dopplergeschwindigkeit konnte ich zeigen, dass das Signal-zu-Rausch Verhältnis von Supergranulation in der Zeit-Entfernungs Helioseismologie nicht stark von Datenkompression beeinflusst wird. Außerdem habe ich nachgewiesen, dass die Genauigkeit und Präzision von Messungen der Sonnenrotation mittels Local Correlation Tracking von Granulation durch verlust- behaftete Datenkompression nicht wesentlich verschlechtert werden. Diese Ergebnisse deuten an, dass die niedrige Datenübertragungsrate von Solar Orbiter nicht unbedingt ein großes Hinderniss für Helioseismologie sein muss

3D Reconstruction of Small Solar System Bodies using Rendered and Compressed Images

Synthetic image generation and reconstruction of Small Solar System Bodies and the influence of compression is becoming an important study topic because of the advent of small spacecraft in deep space missions. Most of these missions are fly-by scenarios, for example in the Comet Interceptor mission. Due to limited data budgets of small satellite missions, maximising scientific return requires investigating effects of lossy compression. A preliminary simulation pipeline had been developed that uses physics-based rendering in combination with procedural terrain generation to overcome limitations of currently used methods for image rendering like the Hapke model. The rendered Small Solar System Body images are combined with a star background and photometrically calibrated to represent realistic imagery. Subsequently, a Structure-from-Motion pipeline reconstructs three-dimensional models from the rendered images. In this work, the preliminary simulation pipeline was developed further into the Space Imaging Simulator for Proximity Operations software package and a compression package was added. The compression package was used to investigate effects of lossy compression on reconstructed models and the possible amount of data reduction of lossy compression to lossless compression. Several scenarios with varying fly-by distances ranging from 50 km to 400 km and body sizes of 1 km and 10 km were simulated and compressed with lossless and several quality levels of lossy compression using PNG and JPEG 2000 respectively. It was found that low compression ratios introduce artefacts resembling random noise while high compression ratios remove surface features. The random noise artefacts introduced by low compression ratios frequently increased the number of vertices and faces of the reconstructed three-dimensional model

Automatic Adaptive Lossy Compression of Multichannel Remote Sensing Images

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted

Earth imaging with microsatellites: An investigation, design, implementation and in-orbit demonstration of electronic imaging systems for earth observation on-board low-cost microsatellites.

This research programme has studied the possibilities and difficulties of using 50 kg microsatellites to perform remote imaging of the Earth. The design constraints of these missions are quite different to those encountered in larger, conventional spacecraft. While the main attractions of microsatellites are low cost and fast response times, they present the following key limitations: Payload mass under 5 kg, Continuous payload power under 5 Watts, peak power up to 15 Watts, Narrow communications bandwidths (9.6 / 38.4 kbps), Attitude control to within 5°, No moving mechanics. The most significant factor is the limited attitude stability. Without sub-degree attitude control, conventional scanning imaging systems cannot preserve scene geometry, and are therefore poorly suited to current microsatellite capabilities. The foremost conclusion of this thesis is that electronic cameras, which capture entire scenes in a single operation, must be used to overcome the effects of the satellite's motion. The potential applications of electronic cameras, including microsatellite remote sensing, have erupted with the recent availability of high sensitivity field-array CCD (charge-coupled device) image sensors. The research programme has established suitable techniques and architectures necessary for CCD sensors, cameras and entire imaging systems to fulfil scientific/commercial remote sensing despite the difficult conditions on microsatellites. The author has refined these theories by designing, building and exploiting in-orbit five generations of electronic cameras. The major objective of meteorological scale imaging was conclusively demonstrated by the Earth imaging camera flown on the UoSAT-5 spacecraft in 1991. Improved cameras have since been carried by the KITSAT-1 (1992) and PoSAT-1 (1993) microsatellites. PoSAT-1 also flies a medium resolution camera (200 metres) which (despite complete success) has highlighted certain limitations of microsatellites for high resolution remote sensing. A reworked, and extensively modularised, design has been developed for the four camera systems deployed on the FASat-Alfa mission (1995). Based on the success of these missions, this thesis presents many recommendations for the design of microsatellite imaging systems. The novelty of this research programme has been the principle of designing practical camera systems to fit on an existing, highly restrictive, satellite platform, rather than conceiving a fictitious small satellite to support a high performance scanning imager. This pragmatic approach has resulted in the first incontestable demonstrations of the feasibility of remote sensing of the Earth from inexpensive microsatellites

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

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

Low-power CMOS digital-pixel Imagers for high-speed uncooled PbSe IR applications

This PhD dissertation describes the research and development of a new low-cost medium wavelength infrared MWIR monolithic imager technology for high-speed uncooled industrial applications. It takes the baton on the latest technological advances in the field of vapour phase deposition (VPD) PbSe-based medium wavelength IR (MWIR) detection accomplished by the industrial partner NIT S.L., adding fundamental knowledge on the investigation of novel VLSI analog and mixed-signal design techniques at circuit and system levels for the development of the readout integrated device attached to the detector. The work supports on the hypothesis that, by the use of the preceding design techniques, current standard inexpensive CMOS technologies fulfill all operational requirements of the VPD PbSe detector in terms of connectivity, reliability, functionality and scalability to integrate the device. The resulting monolithic PbSe-CMOS camera must consume very low power, operate at kHz frequencies, exhibit good uniformity and fit the CMOS read-out active pixels in the compact pitch of the focal plane, all while addressing the particular characteristics of the MWIR detector: high dark-to-signal ratios, large input parasitic capacitance values and remarkable mismatching in PbSe integration. In order to achieve these demands, this thesis proposes null inter-pixel crosstalk vision sensor architectures based on a digital-only focal plane array (FPA) of configurable pixel sensors. Each digital pixel sensor (DPS) cell is equipped with fast communication modules, self-biasing, offset cancellation, analog-to-digital converter (ADC) and fixed pattern noise (FPN) correction. In-pixel power consumption is minimized by the use of comprehensive MOSFET subthreshold operation. The main aim is to potentiate the integration of PbSe-based infra-red (IR)-image sensing technologies so as to widen its use, not only in distinct scenarios, but also at different stages of PbSe-CMOS integration maturity. For this purpose, we posit to investigate a comprehensive set of functional blocks distributed in two parallel approaches: • Frame-based “Smart” MWIR imaging based on new DPS circuit topologies with gain and offset FPN correction capabilities. This research line exploits the detector pitch to offer fully-digital programmability at pixel level and complete functionality with input parasitic capacitance compensation and internal frame memory. • Frame-free “Compact”-pitch MWIR vision based on a novel DPS lossless analog integrator and configurable temporal difference, combined with asynchronous communication protocols inside the focal plane. This strategy is conceived to allow extensive pitch compaction and readout speed increase by the suppression of in-pixel digital filtering, and the use of dynamic bandwidth allocation in each pixel of the FPA. In order make the electrical validation of first prototypes independent of the expensive PbSe deposition processes at wafer level, investigation is extended as well to the development of affordable sensor emulation strategies and integrated test platforms specifically oriented to image read-out integrated circuits. DPS cells, imagers and test chips have been fabricated and characterized in standard 0.15μm 1P6M, 0.35μm 2P4M and 2.5μm 2P1M CMOS technologies, all as part of research projects with industrial partnership. The research has led to the first high-speed uncooled frame-based IR quantum imager monolithically fabricated in a standard VLSI CMOS technology, and has given rise to the Tachyon series [1], a new line of commercial IR cameras used in real-time industrial, environmental and transportation control systems. The frame-free architectures investigated in this work represent a firm step forward to push further pixel pitch and system bandwidth up to the limits imposed by the evolving PbSe detector in future generations of the device.La present tesi doctoral descriu la recerca i el desenvolupament d'una nova tecnologia monolítica d'imatgeria infraroja de longitud d'ona mitja (MWIR), no refrigerada i de baix cost, per a usos industrials d'alta velocitat. El treball pren el relleu dels últims avenços assolits pel soci industrial NIT S.L. en el camp dels detectors MWIR de PbSe depositats en fase vapor (VPD), afegint-hi coneixement fonamental en la investigació de noves tècniques de disseny de circuits VLSI analògics i mixtes pel desenvolupament del dispositiu integrat de lectura unit al detector pixelat. Es parteix de la hipòtesi que, mitjançant l'ús de les esmentades tècniques de disseny, les tecnologies CMOS estàndard satisfan tots els requeriments operacionals del detector VPD PbSe respecte a connectivitat, fiabilitat, funcionalitat i escalabilitat per integrar de forma econòmica el dispositiu. La càmera PbSe-CMOS resultant ha de consumir molt baixa potència, operar a freqüències de kHz, exhibir bona uniformitat, i encabir els píxels actius CMOS de lectura en el pitch compacte del pla focal de la imatge, tot atenent a les particulars característiques del detector: altes relacions de corrent d'obscuritat a senyal, elevats valors de capacitat paràsita a l'entrada i dispersions importants en el procés de fabricació. Amb la finalitat de complir amb els requisits previs, es proposen arquitectures de sensors de visió de molt baix acoblament interpíxel basades en l'ús d'una matriu de pla focal (FPA) de píxels actius exclusivament digitals. Cada píxel sensor digital (DPS) està equipat amb mòduls de comunicació d'alta velocitat, autopolarització, cancel·lació de l'offset, conversió analògica-digital (ADC) i correcció del soroll de patró fixe (FPN). El consum en cada cel·la es minimitza fent un ús exhaustiu del MOSFET operant en subllindar. L'objectiu últim és potenciar la integració de les tecnologies de sensat d'imatge infraroja (IR) basades en PbSe per expandir-ne el seu ús, no només a diferents escenaris, sinó també en diferents estadis de maduresa de la integració PbSe-CMOS. En aquest sentit, es proposa investigar un conjunt complet de blocs funcionals distribuïts en dos enfocs paral·lels: - Dispositius d'imatgeria MWIR "Smart" basats en frames utilitzant noves topologies de circuit DPS amb correcció de l'FPN en guany i offset. Aquesta línia de recerca exprimeix el pitch del detector per oferir una programabilitat completament digital a nivell de píxel i plena funcionalitat amb compensació de la capacitat paràsita d'entrada i memòria interna de fotograma. - Dispositius de visió MWIR "Compact"-pitch "frame-free" en base a un novedós esquema d'integració analògica en el DPS i diferenciació temporal configurable, combinats amb protocols de comunicació asíncrons dins del pla focal. Aquesta estratègia es concep per permetre una alta compactació del pitch i un increment de la velocitat de lectura, mitjançant la supressió del filtrat digital intern i l'assignació dinàmica de l'ample de banda a cada píxel de l'FPA. Per tal d'independitzar la validació elèctrica dels primers prototips respecte a costosos processos de deposició del PbSe sensor a nivell d'oblia, la recerca s'amplia també al desenvolupament de noves estratègies d'emulació del detector d'IR i plataformes de test integrades especialment orientades a circuits integrats de lectura d'imatge. Cel·les DPS, dispositius d'imatge i xips de test s'han fabricat i caracteritzat, respectivament, en tecnologies CMOS estàndard 0.15 micres 1P6M, 0.35 micres 2P4M i 2.5 micres 2P1M, tots dins el marc de projectes de recerca amb socis industrials. Aquest treball ha conduït a la fabricació del primer dispositiu quàntic d'imatgeria IR d'alta velocitat, no refrigerat, basat en frames, i monolíticament fabricat en tecnologia VLSI CMOS estàndard, i ha donat lloc a Tachyon, una nova línia de càmeres IR comercials emprades en sistemes de control industrial, mediambiental i de transport en temps real.Postprint (published version