The 1995 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Segmentation d'images couleur par combinaison LPE-régions/LPE-contours et fusion de régions. Application à la segmentation de toitures à partir d'orthophotoplans

D un point de vue général, les travaux de recherche de cette thèse s inscrivent dans le cadre d une approche globale quiconsiste à extraire des informations relatives aux toitures de bâtiments à partir de photos aériennes (orthophotoplans). L objectifétant de pouvoir reconnaître des toitures extraites d images aériennes en utilisant une base de connaissances, puisaffiner/déformer des modèles 3D générés automatiquement à partir de données géographiques. Pour cela, une premièreétape consiste tout d abord à partitionner l image aérienne en différentes régions d intérêt (pans de toiture, cheminées,chiens assis, fenêtres, etc.), c est la contribution de cette thèse.La méthodologie permettant d atteindre cet objectif est composée de trois étapes : (i) Une étape de simplification qui consisteà simplifier l image initiale avec un couple invariant/gradient approprié et optimisé pour l application. Pour cela, unesérie de tests permettant de choisir, d une part, l invariant colorimétrique le plus approprié parmi 24 invariants et, d autrepart, le meilleur gradient parmi 14 gradients issus de la littérature est réalisée. (ii) La deuxième étape comporte deux stratégiesde segmentation par LPE. L image simplifiée est segmentée d une part par une LPE-régions couplée à une stratégiede fusion de régions, et d autre part, par une LPE-contours. Le processus de fusion de régions intègre des critères defusion fondés sur des grandeurs radiométriques et géométriques adaptés aux particularités des orthophotoplans traités.Une technique de caractérisation 2D des arêtes de toitures par une analyse des segments est proposée afin de calculerl un des critères de fusion. (iii) La troisième étape consiste à combiner les avantages de chaque méthode dans un mêmeschéma de segmentation coopératif afin d aboutir à un résultat de segmentation fiable. Les tests ont été effectués sur unorthophotoplan contenant 100 toitures de complexité variée et évaluées avec le critère de VINET utilisant une segmentationde référence afin de prouver la robustesse et la fiabilité de l approche proposée. Une étape de comparaison permettantde situer les résultats obtenus via notre approche proposée par rapport à ceux obtenus pas les principales méthodes desegmentation de la littérature est finalement effectuée.The work presented in this thesis is developed in a global approach that consists in recognizing roofs extracted from aerialimages using a knowledge database, and bending out 3D models automatically generated from geographical data. Themain step presented in this thesis consists in segmenting roof images in different regions of interest in order to provideseveral measures of roofs (section of roofs, chimneys, roof light, etc).The method aimed at achieving this goal is composed of three principal steps: (i) A simplification step that consists insimplifying the image with an appropriate (optimized for the application) couple of invariant/gradient. For that, several testshave been performed to choose a suitable colorimetric invariant among a set of 24 invariants and define the best gradientamong 14 gradients (eight gray level gradients and six color gradients) of the literature. (ii) The second step is composedof two main treatments: On the one hand, the preliminary orthophotoplan segmentation is produced using the watershedregions applied on the simplified image. An efficient region merging strategy is then applied in order to deal with theover-segmentation problem. The regions merging procedure includes a merging criteria adapted to the orthophotoplanparticularities. In order to calculate one of the merging criteria, a 2D modeling of roof ridges strategy is proposed. Onthe other hand, the simplified image is segmented by the watershed lines. (iii) The third step consists in integrating bothsegmentation strategies by watershed algorithm into a single cooperative segmentation scheme to achieve satisfactorysegmentation results. Tests have been performed on an orthophotoplan containing 100 roofs with varying complexity andevaluated with VINET criteria using a ground truth image segmentation. Comparison results with five popular segmentationtechniques of the literature demonstrates the effectiveness and the reliability of the proposed approach.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences