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Titlebook: Real-Time Recursive Hyperspectral Sample and Band Processing; Algorithm Architectu Chein-I Chang Book 2017 Springer International Publishin

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书目名称Real-Time Recursive Hyperspectral Sample and Band Processing
副标题Algorithm Architectu
编辑Chein-I Chang
视频video
概述Explores recursive structures in algorithm architecture.Implements algorithmic recursive architecture in conjunction with progressive sample and band processing.Derives Recursive Hyperspectral Sample
图书封面Titlebook: Real-Time Recursive Hyperspectral Sample and Band Processing; Algorithm Architectu Chein-I Chang Book 2017 Springer International Publishin
描述.This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, .Real-Time Progressive Hyperspectral Image Processing., published by Springer in 2016..
出版日期Book 2017
关键词Casual hyperspectral image processing; Hyperspectral data analysis; Hyperspectral imaging; Progressive
版次1
doihttps://doi.org/10.1007/978-3-319-45171-8
isbn_softcover978-3-319-83230-2
isbn_ebook978-3-319-45171-8
copyrightSpringer International Publishing Switzerland 2017
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Target-Specified Virtual Dimensionality for Hyperspectral Imagerywer Academic/Plenum Publishers, New York, 2003) and later written about with details in Chang and Du (IEEE Transactions on Geoscience and Remote Sensing 42:608–619, 2004). It was originally developed for the purpose of finding an appropriate number of signatures required by linear spectral mixture a
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Real-Time Recursive Hyperspectral Sample Processing for Active Target Detection: Constrained Energy 016) hyperspectral target detection can be generally performed in two completely opposite modes, active hyperspectral target detection and passive hyperspectral target detection. Active hyperspectral target detection requires specific prior knowledge that can be used to detect targets of interest as
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Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detectioneloped for its real-time and causal implementation. Rather than CEM, this chapter focuses on passive hyperspectral target detection and investigates a commonly used passive target detection technique, anomaly detection (AD), especially for real-time and causal processing capabilities that are develo
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Recursive Hyperspectral Sample Processing of Automatic Target Generation Processsed in a wide range of applications in hyperspectral image analysis to find unknown targets and endmembers. Since it is a pixel-based technique, it can be very easily implemented in real time. In addition, because it is also unsupervised, it can be used to find unknown targets automatically without
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