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001 978-1-4419-6187-7
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008 160322s2016 xxu| s |||| 0|eng d
020 _a9781441961877
_9978-1-4419-6187-7
024 7 _a10.1007/978-1-4419-6187-7
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aChang, Chein-I.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950394
245 1 0 _aReal-Time Progressive Hyperspectral Image Processing
_h[electronic resource] :
_bEndmember Finding and Anomaly Detection /
_cby Chein-I Chang.
250 _a1st ed. 2016.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2016.
300 _aXXIII, 623 p. 331 illus., 256 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aOverview and Introduction -- Part I: Preliminaries -- Linear Spectral Mixture Analysis -- Finding Endmembers in Hyperspectral Imagery -- Linear Spectral Unmixing with Three Criteria, Least Squares Error, Simplex Volume and Orthogonal Projection -- Hyperspectral Target Detection -- Part II: Sample-wise Sequential Processes for Finding Endmembers -- Abundance-Unconstrained Sequential Endmember Finding Algorithms: Orthogonal Projection -- Fully Abundance-Constrained Sequential Endmember Finding Algorithms: Simplex Volume Analysis -- Partially Abundance Non-Negativity-Constrained Endmember Finding Algorithms: Convex Cone Volume Analysis -- Fully Abundance-Constrained Sequential Linear Spectral Mixture Analysis for Finding Endmembers -- Part III: Sample-Wise Progressive Processes for Finding Endmembers -- Abundance-Unconstrained Progressive Endmember Finding Algorithms: Orthogonal Projection -- Fully Abundance-Unconstrained Progressive Endmember Finding Algorithms: Simplex Volume Analysis -- Partially Abundance Non-Negativity-Constrained Progressive Endmember Finding Algorithms: Convex Cone Volume Analysis -- Sully Abundance-Constrained Progressive Linear Spectral Mixture Analysis for Finding Endmembers -- Part IV: Sample-Wise Progressive Unsupervised Target Detection -- Progressive Anomaly Detection -- Progressive Adaptive Anomaly Detection -- Progressive Window-Based Anomaly Detection -- Progressive Subpixel Target Detectio n and Classification.
520 _aThe book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book. Includes preliminary background which is essential to those who work in hyperspectral imaging area Develops sequential and progressive algorithms for finding endmembers as they relate to real time hyperspectral image processing Designs algorithms for anomaly detection from causality and real time perspectives and investigates the effects of causality and real-time processing in anomaly detection.
650 0 _aSignal processing.
_94052
650 0 _aComputer vision.
_950395
650 0 _aPattern recognition systems.
_93953
650 0 _aBiometric identification.
_911407
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputer Vision.
_950396
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aBiometrics.
_932763
710 2 _aSpringerLink (Online service)
_950397
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9781441961860
776 0 8 _iPrinted edition:
_z9781441961884
776 0 8 _iPrinted edition:
_z9781493979257
856 4 0 _uhttps://doi.org/10.1007/978-1-4419-6187-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78583
_d78583