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Titlebook: Optical Remote Sensing; Advances in Signal P Saurabh Prasad,Lori M. Bruce,Jocelyn Chanussot Book 2011 Springer-Verlag Berlin Heidelberg 201

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发表于 2025-3-21 19:22:30 | 显示全部楼层 |阅读模式
书目名称Optical Remote Sensing
副标题Advances in Signal P
编辑Saurabh Prasad,Lori M. Bruce,Jocelyn Chanussot
视频video
概述Presents current state-of-the-art algorithms.Addresses key challenges for an effective exploitation.Written by key scientists in the field.Includes supplementary material:
丛书名称Augmented Vision and Reality
图书封面Titlebook: Optical Remote Sensing; Advances in Signal P Saurabh Prasad,Lori M. Bruce,Jocelyn Chanussot Book 2011 Springer-Verlag Berlin Heidelberg 201
描述Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data.Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.
出版日期Book 2011
关键词high spectral resolution; hyperspectral imagery possessing; multispectral imagery possessing; pre-proce
版次1
doihttps://doi.org/10.1007/978-3-642-14212-3
isbn_softcover978-3-642-26747-5
isbn_ebook978-3-642-14212-3Series ISSN 2190-5916 Series E-ISSN 2190-5924
issn_series 2190-5916
copyrightSpringer-Verlag Berlin Heidelberg 2011
The information of publication is updating

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Hyperspectral Data Compression Tradeoff, is the size of the data: as a third dimension is added, the amount of data increases dramatically making the compression necessary at different steps of the processing chain. Also different properties are required at different stages of the processing chain with variable tradeoff. Second, the diffe
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Integrated Sensing and Processing for Hyperspectral Imagery,relevant to the application. Broadly, integrated sensing and processing (ISP) considers algorithms that are integrated with the collection of data. That is, traditional sensor development tries to come up with the “best” sensor in terms of SNR, resolution, data rates, integration time, and so forth,
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An Evaluation of Visualization Techniques for Remotely Sensed Hyperspectral Imagery,ily render the desired information at arbitrary levels of detail. This chapter discusses user studies on several approaches for representing the information contained in hyperspectral information. In particular, we compared four visualization methods: grayscale side-by-side display (GRAY), hard visu
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The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images,largely proved to be a powerful tool able to model the spatial information (e.g., contextual relations) of the image by extracting structural features (e.g., size, geometry, etc.) from the objects present in the scene. The MP processes an input image with a sequence of progressively coarser filters.
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Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Meansmote sensing imagery. It is very normal that different classification schemes yield slightly different results for different classes. This effect is even more prominent in vegetation mapping applications due to the inconsistent spectral signatures of the vegetation classes. We study the possibility
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A Review of Kernel Methods in Remote Sensing Data Analysis,stent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are par
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