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Titlebook: Computational Intelligence for Remote Sensing; Manuel Gra?a,Richard J. Duro Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Markov

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書(shū)目名稱(chēng)Computational Intelligence for Remote Sensing
編輯Manuel Gra?a,Richard J. Duro
視頻videohttp://file.papertrans.cn/233/232460/232460.mp4
概述Presents recent results in computational intelligence applications in remote sensing.Includes supplementary material:
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Computational Intelligence for Remote Sensing;  Manuel Gra?a,Richard J. Duro Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Markov
描述.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..
出版日期Book 2008
關(guān)鍵詞Markov; Markov random field; classification; cognition; computational intelligence; evolutionary algorith
版次1
doihttps://doi.org/10.1007/978-3-540-79353-3
isbn_softcover978-3-642-09823-9
isbn_ebook978-3-540-79353-3Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Remote Sensing Data Compression,e a special challenge, and multiple efficient image compression systems have appeared. This chapter contributes an overview of several techniques for image coding systems, focusing on lossy approaches.
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On Content-Based Image Retrieval Systems for Hyperspectral Remote Sensing Images, the literature regarding remote sensing images. Our own focus is on hyperspectral images. The approach we are pursuing is that of characterizing the spectral content of the image through the set of endmembers induced from it. We describe some ideas and numerical experiments for a system that would
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Parallel Classification of Hyperspectral Images Using Neural Networks,est-generation Earth observation instruments is expected to introduce extremely high computational requirements in neural network-based algorithms for classification of high-dimensional data sets such as hyperspectral images, with hundreds of spectral channels and very fine spatial resolution. A sig
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