International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The next-generation commercial imaging satellites will generate data at several times the rate of current systems. To be commercially successful, these systems must have earth stations as sophisticated as the satellites themselves. Space Imaging has worked with E-Systems to exploit technologies developed over four generations of image processing, analysis and application systems to create a modular, standards-based, earth station for commercial use. A Space Imaging Operations Center can be configured in a variety of ways to provide complete, end-to-end, capabilities, from task generation to receipt of downlink, image processing, and product generation. While it is intended primarily for use with imagery from Space Imaging and other commercial satellites, an Operations Center can also accept, process and manage data from land-based, airborne or seaborne collectors. A sophisticated data management product, Mission Server™, handles and routes all data from signal receipt through final product generation. A unique family of data processing applications permit simultaneous manipulation and analysis of integrated map, image, graphic and text data. Online data storage and archiving are provided by the EMASS® family of products. An Operations Center of any size can accept, process and manage data streams of several hundred megabits per second in real time.

Le sujet de cette thèse concerne le codage et la restauration d'image dans le contexte de l'imagerie satellite. En dépit des récents développements en restauration et compression embarquée d'images, de nombreux artéfacts apparaissent dans la reconstruction de l'image. L'objectif de cette thèse est d'améliorer la qualité de l'image finale en étudiant la structure optimale de décodage et de restauration en fonction des caractéristiques des processus d'acquisition et de compression. Plus globalement, le but de cette thèse est de proposer une méthode efficace permettant de résoudre le problème de décodage-déconvolution-débruitage optimal dans un objectif d'optimisation globale de la chaîne compression/restauration. Le manuscrit est organisé en trois parties. La première partie est une introduction générale à la problématique traitée dans ce travail. Nous présentons un état de l'art des techniques de restauration et de compression pour l'imagerie satellite et nous décrivons la chaîne de traitement actuellement utilisée par le Centre National d'Etudes Spatiales (CNES) qui servira de référence tout au long de ce manuscrit. La deuxième partie concerne l'optimisation globale de la chaîne e d'imagerie satellite. Nous proposons une approche pour estimer la distorsion théorique de la chaîne complète et développons, dans trois configurations différentes de codage/restauration, un algorithme pour réaliser la minimisation. Notre deuxième contribution met également l'accent sur l'étude la chaîne globale mais est plus ciblée sur l'optimisation de la qualité visuelle de l'image finale. Nous présentons des méthodes numériques permettant d'améliorer la qualité de l'image reconstruite et nous proposons une nouvelle chaîne image basée sur les résultats d'évaluation de qualité de ces techniques. La dernière partie de la thèse introduit une chaîne d'imagerie satellite basée sur une nouvelle théorie de l'échantillonnage. Cette technique d'échantillonnage est intéressante dans le domaine du satellitaire car elle permet de transférer toutes les difficultés au décodeur qui se situe au sol. Nous rappelons les principaux résultats théoriques de cette technique d'échantillonnage et nous présentons une chaîne image construite à partir de cette méthode. Nous proposons un algorithme permettant de résoudre le problème de reconstruction et nous concluons cette partie en comparant les résultats obtenus avec cette chaîne et celle utilisée actuellement par le CNES. / The subject of this work is image coding and restoration in the context of satellite imaging. Regardless of recent developments in image restoration techniques and embedded compression algorithms, the reconstructed image still suffers from coding artifacts making its quality evaluation difficult. The objective of the thesis is to improve the quality of the final image with the study of the optimal structure of decoding and restoration regarding to the properties of the acquisition and compression processes. More essentially, the aim of this work is to propose a reliable technique to address the optimal decoding-deconvolution-denoising problem in the objective of global optimization of the compression/restoration chain. The thesis is organized in three parts. The first part is a general introduction to the problematic addressed in this work. We then review a state-of-the-art of restoration and compression techniques for satellite imaging and we describe the current imaging chain used by the French Space Agency as this is the focus of the thesis. The second part is concerned with the global optimization of the satellite imaging chain. We propose an approach to estimate the theoretical distortion of the complete chain and we present, for three different configurations of coding/restoration, an algorithm to perform its minimization. Our second contribution is also focused on the study of the global chain but is more aimed to optimize the visual quality of the final image. We present numerical methods to improve the quality of the reconstructed image and we propose a novel imaging chain based on the image quality assessment results of these techniques. The last part of the thesis introduces a satellite imaging chain based on a new sampling approach. This approach is interesting in the context of satellite imaging as it allows transferring all the difficulties to the on-ground decoder. We recall the main theoretical results of this sampling technique and we present a satellite imaging chain based on this framework. We propose an algorithm to solve the reconstruction problem and we conclude by comparing the proposed chain to the one currently used by the CNES.

The subject of this work is image coding and restoration in the context of satellite imaging. Regardless of recent developments in image restoration techniques and embedded compression algorithms, the reconstructed image still suffers from coding artifacts making its quality evaluation difficult. The objective of the thesis is to improve the quality of the final image with the study of the optimal structure of decoding and restoration regarding to the properties of the acquisition and compression processes. More essentially, the aim of this work is to propose a reliable technique to address the optimal decoding-deconvolution-denoising problem in the objective of global optimization of the compression/restoration chain. The thesis is organized in three parts. The first part is a general introduction to the problematic addressed in this work. We then review a state-of-the-art of restoration and compression techniques for satellite imaging and we describe the current imaging chain used by the French Space Agency as this is the focus of the thesis. The second part is concerned with the global optimization of the satellite imaging chain. We propose an approach to estimate the theoretical distortion of the complete chain and we present, for three different configurations of coding/restoration, an algorithm to perform its minimization. Our second contribution is also focused on the study of the global chain but is more aimed to optimize the visual quality of the final image. We present numerical methods to improve the quality of the reconstructed image and we propose a novel imaging chain based on the image quality assessment results of these techniques. The last part of the thesis introduces a satellite imaging chain based on a new sampling approach. This approach is interesting in the context of satellite imaging as it allows transferring all the difficulties to the on-ground decoder. We recall the main theoretical results of this sampling technique and we present a satellite imaging chain based on this framework. We propose an algorithm to solve the reconstruction problem and we conclude by comparing the proposed chain to the one currently used by the CNES.

A necessidade de novas técnicas para a obtenção de informações sobre os solos e seus atributos, de forma mais rápida e menos onerosa, tornaram o sensoriamento remoto e o geoprocessamento uma importante opção. Estas técnicas permitem analisar uma grande quantidade de dados ao mesmo tempo e associar as informações espectrais com outras variáveis ambientais como geologia e relevo. Neste sentido, este trabalho teve por objetivos (i) caracterizar o comportamento espectral de solos pelos sensores orbitais ASTER e TM e o sensor terrestre IRIS; (ii) avaliar o potencial em estimar teores de atributos do solo por meio de dados espectrais orbitais ASTER em conjunto com os topográficos (avaliação pontual); (iii) determinar a distribuição espacial de atributos do solo pela imagem ASTER (avaliação espacial). Para isso, foram utilizadas informações georeferenciadas de 184 pontos de amostragem de solo da região de Rafard, SP. Também foram utilizadas as análises químicas e físicas das amostras, bem como as informações espectrais orbitais e em laboratório. Na sequência, curvas espectrais médias de solo e de atributos foram geradas para sua caracterização. Modelos estatísticos associando dados de reflectância e topográficos foram gerados para quantificar atributos do solo. Com isso realizou-se o mapeamento dos atributos do solo na imagem de satélite ASTER. Verificou-se que (i) é possível discriminar atributos do solo através de sensores orbitais, sendo que as bandas da faixa do infravermelho se mostraram mais eficazes; o ferro total e matéria orgânica foram os atributos melhor discriminados pelos sensores orbitais ASTER e TM, (ii) é possível quantificar os atributos argila, ferro, silício e titânio utilizando dados espectrais conjuntamente com topográficos, (iii) a quantificação de atributos foi melhor estimada com as variáveis espectrais e topográficas conjuntamente no modelo de regressão quando comparada ao modelo espectral individualmente, (iv) é possível mapear os atributos textura, matéria orgânica, ferro total e capacidade de troca catiônica com índices de até 75 % de similaridade. / The necessity of new techniques to obtain information about soil and its attributes, in a faster and cheaper form, turns remote sensing and geoprocessing into important options. These techniques allow the analysis of a great amount of data at the same time and associate spectral information with other environmental variables such as geology and relief. The objective of this work was (i) to characterize the spectral behavior of soils by orbital (ASTER and TM) and terrestrial (IRIS) sensors; (ii) to evaluate the potencial to estimate soil attributes content through ASTER orbital spectral data combined with topography (punctual evaluation); (iii) to determine the spatial distribution of soil attributes on ASTER image (spatial evaluation). Information was collected from 184 georeferenced soil samples from Rafard, SP. It was also used chemical and physical analyses of the samples as well as laboratory and orbital spectral data. Then, mean spectral curves of soils and attributes were generated for their characterization, statistical models associating reflectance and topography data were created to quantify soil attributes, and attributes maps were done at ASTER image. Results showed that (i) it is possible to discriminate soil attributes through orbital sensors, and the infrared bands were the best ones for this; total iron and organic matter were the best attributes discriminated by ASTER and TM sensors, (ii) it is possible to quantify clay, total iron, silicon and titanium using spectral and topographic data, (iii) the quantification of attributes was better estimated with spectral and topographic data in the models when compared to the models with spectral data only, (iv) it is possible to create maps of grain size distribution, organic matter, total iron and cation exchange capacity with indexes of 75% of similarity.

A necessidade de novas técnicas para a obtenção de informações sobre os solos e seus atributos, de forma mais rápida e menos onerosa, tornaram o sensoriamento remoto e o geoprocessamento uma importante opção. Estas técnicas permitem analisar uma grande quantidade de dados ao mesmo tempo e associar as informações espectrais com outras variáveis ambientais como geologia e relevo. Neste sentido, este trabalho teve por objetivos (i) caracterizar o comportamento espectral de solos pelos sensores orbitais ASTER e TM e o sensor terrestre IRIS; (ii) avaliar o potencial em estimar teores de atributos do solo por meio de dados espectrais orbitais ASTER em conjunto com os topográficos (avaliação pontual); (iii) determinar a distribuição espacial de atributos do solo pela imagem ASTER (avaliação espacial). Para isso, foram utilizadas informações georeferenciadas de 184 pontos de amostragem de solo da região de Rafard, SP. Também foram utilizadas as análises químicas e físicas das amostras, bem como as informações espectrais orbitais e em laboratório. Na sequência, curvas espectrais médias de solo e de atributos foram geradas para sua caracterização. Modelos estatísticos associando dados de reflectância e topográficos foram gerados para quantificar atributos do solo. Com isso realizou-se o mapeamento dos atributos do solo na imagem de satélite ASTER. Verificou-se que (i) é possível discriminar atributos do solo através de sensores orbitais, sendo que as bandas da faixa do infravermelho se mostraram mais eficazes; o ferro total e matéria orgânica foram os atributos melhor discriminados pelos sensores orbitais ASTER e TM, (ii) é possível quantificar os atributos argila, ferro, silício e titânio utilizando dados espectrais conjuntamente com topográficos, (iii) a quantificação de atributos foi melhor estimada com as variáveis espectrais e topográficas conjuntamente no modelo de regressão quando comparada ao modelo espectral individualmente, (iv) é possível mapear os atributos textura, matéria orgânica, ferro total e capacidade de troca catiônica com índices de até 75 % de similaridade. / The necessity of new techniques to obtain information about soil and its attributes, in a faster and cheaper form, turns remote sensing and geoprocessing into important options. These techniques allow the analysis of a great amount of data at the same time and associate spectral information with other environmental variables such as geology and relief. The objective of this work was (i) to characterize the spectral behavior of soils by orbital (ASTER and TM) and terrestrial (IRIS) sensors; (ii) to evaluate the potencial to estimate soil attributes content through ASTER orbital spectral data combined with topography (punctual evaluation); (iii) to determine the spatial distribution of soil attributes on ASTER image (spatial evaluation). Information was collected from 184 georeferenced soil samples from Rafard, SP. It was also used chemical and physical analyses of the samples as well as laboratory and orbital spectral data. Then, mean spectral curves of soils and attributes were generated for their characterization, statistical models associating reflectance and topography data were created to quantify soil attributes, and attributes maps were done at ASTER image. Results showed that (i) it is possible to discriminate soil attributes through orbital sensors, and the infrared bands were the best ones for this; total iron and organic matter were the best attributes discriminated by ASTER and TM sensors, (ii) it is possible to quantify clay, total iron, silicon and titanium using spectral and topographic data, (iii) the quantification of attributes was better estimated with spectral and topographic data in the models when compared to the models with spectral data only, (iv) it is possible to create maps of grain size distribution, organic matter, total iron and cation exchange capacity with indexes of 75% of similarity.

Throughout the years of space exploration, the usage of Earth observation satellites has increased tremendously. The usage today extends beyond optical sensors, encompassing radars, infrared, and laser sensors. For this thesis, the usage of optical, synthetic aperture radar, and LiDAR sensors were looked at to see if the fusion of these different sensors could enhance the overall image quality. A crucial aspect of satellite image fusion, regardless of sensor type, is preprocessing to ensure the individual images can be seamlessly merged. Ultimately these preprocessing steps are individual to both the sensors and even different satellites. The topic of remote sensing and satellite image fusion is extensive and complex. Therefore, this thesis aims to explore various fusion techniques, data sources, and algorithms to contribute to a deeper understanding of the advantages but mostly challenges associated with multisensor satellite image fusion. A web scraper was developed to collect data from the European Space Agency’s Third Party Mission website, a central repository for satellite missions and Earth images. The scraper made it possible to select different satellites and find the image areas which they had in common. A way to process this data is then presented on how to process the images and finally fuse them. The three fusion algorithms that were used were a simple weighted average, intensity hue saturation, and the pansharpening method. The pansharpening increased both the spatial and spectral resolution whereas the fusion of the optical and synthetic aperture radar gave some mixed results. There are a lot of things that could be explored in the future, such as utilizing more complex fusion algorithms or using additional satellite sensors. However, the web scraper and the processing flowchart stand as notable achievements of this thesis, simplifying the entire process of multisensor satellite image fusion. / Genom åren har användningen av jordobservationsatelliter ökat avsevärt inom rymdforskning. Användningen sträcker sig idag bortom optiska sensorer och inkluderar även radar-, infraröd- och lasersensorer. I detta examensarbete undersöks användningen av optiska, syntetisk aperturradar- och LiDARsensorer för att se om fusionen av dessa olika sensorer kan förbättra helhetsbilden av ett område. En avgörande aspekt av satellitbildsfusion, oavsett sensortyp, är förbehandling för att säkerställa att de individuella bilderna kan smidigt integreras. Slutligen är dessa förbehandlingsteg specifika för både sensorerna och olika satelliter. Ämnet fjärranalys och fusion av satellitbilder är omfattande och komplext. Därför syftar detta examensarbete till att utforska olika fusionsmetoder, datakällor och algoritmer för att bidra till en djupare förståelse för fördelarna och utmaningarna med multisensor fusion av satellitbilder. Ett av huvudproblemen under examensarbetet var datainsamling och databehandling. För att överkomma detta utvecklades en webbskrapare för att samla in data från European Space Agencys Third Party Mission hemsida, en central databas för satellituppdrag och bilder av jorden. Skrapan möjliggjorde valet av olika satelliter och identifieringen av gemensamma bildområden. En metod för databehandling presenteras sedan för att bearbeta bilderna och slutligen förena dem. De tre fusionsalgoritmerna som användes var en enkel viktad medelvärdesmetod, intensitetssättning och pansharpening. Pansharpening ökade både den spatiala och spektrala upplösningen, medan fusionen av optiska och syntetisk aperturradar gav blandade resultat. Det finns många områden som kan utforskas i framtiden, såsom användning av mer komplexa fusionsalgoritmer eller ytterligare satellitsensorer. Sammanfattande kan webbskrapan och behandlingsflödet ses som betydande framsteg i detta examensarbete och förhoppningsvis förenkla hela processen med multisensor fusion av satellitbilder.