LEADER 05319nam 22006975 450 001 9910253973903321 005 20200703110342.0 010 $a1-4419-6187-9 024 7 $a10.1007/978-1-4419-6187-7 035 $a(CKB)3710000000620523 035 $a(EBL)4455159 035 $a(SSID)ssj0001654150 035 $a(PQKBManifestationID)16433972 035 $a(PQKBTitleCode)TC0001654150 035 $a(PQKBWorkID)14982759 035 $a(PQKB)11494100 035 $a(DE-He213)978-1-4419-6187-7 035 $a(MiAaPQ)EBC4455159 035 $a(PPN)192770071 035 $a(EXLCZ)993710000000620523 100 $a20160322d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aReal-Time Progressive Hyperspectral Image Processing $eEndmember Finding and Anomaly Detection /$fby Chein-I Chang 205 $a1st ed. 2016. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2016. 215 $a1 online resource (626 p.) 300 $aDescription based upon print version of record. 311 $a1-4419-6186-0 320 $aIncludes bibliographical references. 327 $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. 330 $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. 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aPattern perception 606 $aBiometry 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aBiometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22040 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 0$aPattern perception. 615 0$aBiometry. 615 14$aSignal, Image and Speech Processing. 615 24$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aBiometrics. 676 $a620 700 $aChang$b Chein-I$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763028 906 $aBOOK 912 $a9910253973903321 996 $aReal-Time Progressive Hyperspectral Image Processing$91547616 997 $aUNINA