000			04308nam a22003615i 4500
001			84379
003			ES-MaUEC
005			20230207040420.0
007			cr nn 008mamaa
008			160322s2016 xxu| s |||| 0|eng d
020			_a9781441961877
040			_aES-MaUEC
050		4	_aTA1637 _b.C436 2016
100	1		_aChang, Chein-I _0Local _996501
245	1	0	_aReal-Time Progressive Hyperspectral Image Processing : _bEndmember Finding and Anomaly Detection _cby Chein-I Chang
260			_aNew York, NY _bSpringer New York _c2016
300			_a1 recurso en línea (XXIII, 623 páginas) _b331 ilustraciones, 256 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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	3		_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.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER,
650	0	4	_9669495 _aProceso de imágenes _0
650	0	7	_aReconocimiento de formas _0 _2embne _9152614
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-1-4419-6187-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9781441961877
907			_a.b12936571 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _gxxu _h0
945			_aTA1637 .C436 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11581542 _z06-04-17
999			_c84379 _d84379 _x1