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Taschenbuch. Zustand: Neu. Computational Intelligence for Remote Sensing | Manuel Grana (u. a.) | Taschenbuch | x | Englisch | 2010 | Springer | EAN 9783642098239 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a composition of different points of view regarding the application of Computational Intelligence techniques and methods to Remote Sensing data and applications. It is the general consensus that classification, its related data processing, and global optimization methods are core topics of Computational Intelligence. Much of the content of the book is devoted to image segmentation and recognition, using diverse tools from different areas of the Computational Intelligence field, ranging from Artificial Neural Networks to Markov Random Field modeling. The book covers a broad range of topics, starting from the hardware design of hyperspectral sensors, and data handling problems, namely data compression and watermarking issues, as well as autonomous web services. The main contents of the book are devoted to image analysis and efficient (parallel) implementations of these analysis techniques. The classes of images dealt with throughout the book are mostly multispectral-hyperspectral images, though there are some instances of processing Synthetic Aperture Radar images.
Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2010
ISBN 10: 3642098231 ISBN 13: 9783642098239
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a composition of different points of view regarding the application of Computational Intelligence techniques and methods to Remote Sensing data and applications. It is the general consensus that classification, its related data processing, and global optimization methods are core topics of Computational Intelligence. Much of the content of the book is devoted to image segmentation and recognition, using diverse tools from different areas of the Computational Intelligence field, ranging from Artificial Neural Networks to Markov Random Field modeling. The book covers a broad range of topics, starting from the hardware design of hyperspectral sensors, and data handling problems, namely data compression and watermarking issues, as well as autonomous web services. The main contents of the book are devoted to image analysis and efficient (parallel) implementations of these analysis techniques. The classes of images dealt with throughout the book are mostly multispectral-hyperspectral images, though there are some instances of processing Synthetic Aperture Radar images.
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Hardcover. Zustand: new. Hardcover. With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpretation of such data are increasingly challenged by large volumes, complex spatial-spectral structures, and limited labeled samples. Computational Intelligence (CI), inspired by natural and biological systems, offers promising strategies to address these complexities through adaptive learning, feature extraction, and decision-making.This reprint explores the latest CI-driven methodologies applied to remote sensing tasks, as reflected in recent advancements such as hybrid retrieval systems, lightweight segmentation networks, few-shot classification models, semantic-enhanced image captioning, and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS, including pan-sharpening, road extraction, radar imaging, and hyperspectral unmixing. By integrating theory with state-of-the-art applications, the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Zustand: new. Hardcover. With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpretation of such data are increasingly challenged by large volumes, complex spatial-spectral structures, and limited labeled samples. Computational Intelligence (CI), inspired by natural and biological systems, offers promising strategies to address these complexities through adaptive learning, feature extraction, and decision-making.This reprint explores the latest CI-driven methodologies applied to remote sensing tasks, as reflected in recent advancements such as hybrid retrieval systems, lightweight segmentation networks, few-shot classification models, semantic-enhanced image captioning, and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS, including pan-sharpening, road extraction, radar imaging, and hyperspectral unmixing. By integrating theory with state-of-the-art applications, the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbHardcover. Zustand: new. Hardcover. With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpretation of such data are increasingly challenged by large volumes, complex spatial-spectral structures, and limited labeled samples. Computational Intelligence (CI), inspired by natural and biological systems, offers promising strategies to address these complexities through adaptive learning, feature extraction, and decision-making.This reprint explores the latest CI-driven methodologies applied to remote sensing tasks, as reflected in recent advancements such as hybrid retrieval systems, lightweight segmentation networks, few-shot classification models, semantic-enhanced image captioning, and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS, including pan-sharpening, road extraction, radar imaging, and hyperspectral unmixing. By integrating theory with state-of-the-art applications, the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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