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Artificial Neural Networks And Evolutionary Computation In Remote Sensing
Anbieter: GreatBookPrices, Columbia, MD, USA
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing (Hardback or Cased Book)
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Hardback or Cased Book. Zustand: New. Artificial Neural Networks and Evolutionary Computation in Remote Sensing. Book.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
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Artificial Neural Networks And Evolutionary Computation In Remote Sensing
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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EUR 57,85
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Artificial Neural Networks And Evolutionary Computation In Remote Sensing
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EUR 55,81
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Artificial Neural Networks And Evolutionary Computation In Remote Sensing
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Classification Methods for Remotely Sensed Data
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
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EUR 86,26
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Classification Methods for Remotely Sensed Data
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Zustand: New.
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Classification Methods for Remotely Sensed Data
Anbieter: Books From California, Simi Valley, CA, USA
Verkäuferbewertung 4 von 5 Sternen
hardcover. Zustand: Fine.
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
Verkäuferbewertung 4 von 5 Sternen
EUR 142,53
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Classification Methods for Remotely Sensed Data
Anbieter: GreatBookPrices, Columbia, MD, USA
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EUR 190,47
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Classification Methods for Remotely Sensed Data
Anbieter: GreatBookPrices, Columbia, MD, USA
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EUR 229,79
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Zustand: New. 3rd edition NO-PA16APR2015-KAP.
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Classification Methods for Remotely Sensed Data
Anbieter: California Books, Miami, FL, USA
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Classification Methods for Remotely Sensed Data
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
EUR 233,35
EUR 17,59 Versand
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In den WarenkorbZustand: As New. Unread book in perfect condition.
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Classification Methods for Remotely Sensed Data
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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EUR 242,26
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Classification Methods for Remotely Sensed Data
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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EUR 242,25
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Classification Methods for Remotely Sensed Data
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
EUR 318,93
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In den WarenkorbHardcover. Zustand: Brand New. 3rd edition. 520 pages. 10.00x7.00x10.00 inches. In Stock.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: PBShop.store US, Wood Dale, IL, USA
Verkäuferbewertung 5 von 5 Sternen
HRD. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
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EUR 55,82
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In den WarenkorbHRD. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Verkäuferbewertung 4 von 5 Sternen
EUR 74,81
EUR 7,62 Versand
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In den WarenkorbZustand: New. Print on Demand pp. 256.
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Zustand: New. Print on Demand pp. 256.
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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. PRINT ON DEMAND pp. 256.
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Classification Methods for Remotely Sensed Data (Paperback)
Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2026
ISBN 10: 1032573953 ISBN 13: 9781032573953
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.New in this edition:Provides comprehensive background on the theory of deep learning and its application to remote sensing data.Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications.Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies.Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models.This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc. The new edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. 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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Classification Methods for Remotely Sensed Data (Paperback)
Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2026
ISBN 10: 1032573953 ISBN 13: 9781032573953
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
EUR 96,62
EUR 43,39 Versand
Versand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: new. Paperback. The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.New in this edition:Provides comprehensive background on the theory of deep learning and its application to remote sensing data.Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications.Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies.Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models.This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc. The new edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. 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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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
