Computational Intelligence Remote Sensing (72 results)

Paperback. Condition: new. Paperback. Nowadays, remote sensing systems and technologies have been widely studied and applied in environmental monitoring, land survey, and disaster management. As a pivotal remote sensing task, change detection aims to identify and quantify spatio-temporal changes using multi-temporal imagery, supporting timely decision-making and sustainable resource planning. Nevertheless, conventional change detection approaches remain limited in addressing challenges including sensitivity to noise, discrepancies in spatial resolution, sensor misalignment, and the fusion of multi-source heterogeneous data. To address these issues, advanced computational intelligence (CI) techniques, particularly deep learning and evolutionary computation, are being increasingly adopted, offering improved robustness and adaptability for modern change detection tasks.This book establishes the first systematic framework of CI-driven methodologies in remote sensing change detection, providing a comprehensive exposition spanning theoretical foundations, algorithmic innovation, and empirical validation. Opening with the research principles of remote sensing change detection and core CI theories, it covers CI-driven methodologies tailored to homogeneous (e.g., single-sensor time series) and heterogeneous (e.g., cross-sensor) paradigms. These methodologies address domain-critical challenges such as noise robustness, feature space alignment, and multi-source fusion through rigorously designed technical workflows that cover data preprocessing, adaptive model learning, and task-specific network architecture. Extensive validation across diverse remote sensing data typesincluding synthetic aperture radar, optical, multispectral, and hyperspectral imageryempirically confirms the operational efficacy of these methodologies in delivering accurate and robust change monitoring. By bridging theory and practice, this book empowers readers to formulate complex problems, develop robust models, and apply cutting-edge CI techniques to remote sensing change detection tasks. It is ideal for researchers and engineers working at the intersection of remote sensing, machine learning, and computational intelligence who seek practical and scalable solutions for change detection in evolving environments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condition: Brand New. 159 pages. 6.14x0.34x9.21 inches. In Stock.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nowadays, remote sensing systems and technologies have been widely studied and applied in environmental monitoring, land survey, and disaster management. As a pivotal remote sensing task, change detection aims to identify and quantify spatio-temporal changes using multi-temporal imagery, supporting timely decision-making and sustainable resource planning. Nevertheless, conventional change detection approaches remain limited in addressing challenges including sensitivity to noise, discrepancies in spatial resolution, sensor misalignment, and the fusion of multi-source heterogeneous data. To address these issues, advanced computational intelligence (CI) techniques, particularly deep learning and evolutionary computation, are being increasingly adopted, offering improved robustness and adaptability for modern change detection tasks.This book establishes the first systematic framework of CI-driven methodologies in remote sensing change detection, providing a comprehensive exposition spanning theoretical foundations, algorithmic innovation, and empirical validation. Opening with the research principles of remote sensing change detection and core CI theories, it covers CI-driven methodologies tailored to homogeneous (e.g., single-sensor time series) and heterogeneous (e.g., cross-sensor) paradigms. These methodologies address domain-critical challenges such as noise robustness, feature space alignment, and multi-source fusion through rigorously designed technical workflows that cover data preprocessing, adaptive model learning, and task-specific network architecture. Extensive validation across diverse remote sensing data types including synthetic aperture radar, optical, multispectral, and hyperspectral imagery empirically confirms the operational efficacy of these methodologies in delivering accurate and robust change monitoring.By bridging theory and practice, this book empowers readers to formulate complex problems, develop robust models, and apply cutting-edge CI techniques to remote sensing change detection tasks. It is ideal for researchers and engineers working at the intersection of remote sensing, machine learning, and computational intelligence who seek practical and scalable solutions for change detection in evolving environments.…

Condition: As New. Unread book in perfect condition.

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Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

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Condition: New. pp. 404.

Condition: New. pp. 404 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

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Taschenbuch. Condition: Neu. Computational Intelligence for Remote Sensing | Manuel Grana (u. a.) | Taschenbuch | Studies in Computational Intelligence | 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. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Optical Configurations for Imaging Spectrometers.- Remote Sensing Data Compression.- A Multiobjective Evolutionary Algorithm for Hyperspectral Image Watermarking.- Architecture and Services for Computational Intelligence in Remote Sensing.- On Content-Based Image Retrieval Systems for Hyperspectral Remote Sensing Images.- An Analytical Approach to the Optimal Deployment of Wireless Sensor Networks.- Parallel Spatial-Spectral Processing of Hyperspectral Images.- Parallel Classification of Hyperspectral Images Using Neural Networks.- Positioning Weather Systems from Remote Sensing Data Using Genetic Algorithms.- A Computation Reduced Technique to Primitive Feature Extraction for Image Information Mining Via the Use of Wavelets.- Neural Networks for Land Cover Applications.- Information Extraction for Forest Fires Management.- Automatic Preprocessing and Classification System for High Resolution Ultra and Hyperspectral Images.- Using Gaussian Synapse ANNs for Hyperspectral Image Segmentation and Endmember Extraction.- Unsupervised Change Detection from Multichannel SAR Data by Markov Random Fields.…

Taschenbuch. Condition: 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.…

Condition: New. pp. 404.

Paperback. Condition: Brand New. 404 pages. 9.25x6.10x0.91 inches. In Stock.