000			05496cam a2200421Ii 4500
001			95875
003			ES-MaUEC
005			20230102112726.0
006			m o d
007			cr cnu|||unuuu
008			170426s2017 sz a ob 001 0 eng d
020			_a3319451715 _q(electronic bk.)
020			_a9783319451718 _q(electronic bk.)
020			_z3319451707
020			_z9783319451701 _q(print)
040			_aN$T _cN$T _dEBLCP _dGW5XE _dYDX _dUAB _dN$T _dMERER _dESU _dAZU _dUPM _dOCLCF _dOCLCQ _dVT2 _dOTZ _dOCLCQ _dIOG _dU3W _dES-MaUEC _bspa
050		4	_aTK5102.9 _bC436 2017 EB
100	1		_aChang, Chein-I _eautor _996501
245	1	0	_aReal-time recursive hyperspectral sample and band processing : _balgorithm architecture and implementation _cChein-I Chang.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xxiii, 690 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface; Contents; About the Author; Chapter 1: Introduction; 1.1 Introduction; 1.2 Recursive Hyperspectral Sample Processing ; 1.2.1 Sample Spectral Statistics-Based Recursive Hyperspectral Sample Processing; 1.2.2 Signature Spectral Statistics-Based Recursive Hyperspectral Sample Processing; 1.3 Recursive Hyperspectral Band Processing ; 1.3.1 Band Selection ; 1.3.2 Progressive Hyperspectral Band Processing ; 1.3.3 Recursive Hyperspectral Band Processing ; 1.3.3.1 Sample Spectral Statistics-Based Recursive Hyperspectral Band Processing.
505	8		_a1.3.3.2 Sample Spectral Statistics-Based Recursive Hyperspectral Band Processing1.4 Scope of Book; 1.4.1 Part I: Fundamentals; 1.4.2 Part II: Sample Spectral Statistics-Based Recursive Hyperspectral Sample Processing ; 1.4.3 Part III: Signature Spectral Statistics-Based Recursive Hyperspectral Sample Processing ; 1.4.4 Part IV: Sample Statistics-Based Recursive Hyperspectral Band Processing ; 1.4.5 Part V: Signature Statistics-Based Recursive Hyperspectral Band Processing ; 1.5 Real Hyperspectral Images to Be Used in This Book; 1.5.1 AVIRIS Data; 1.5.1.1 Cuprite Data.
505	8		_a1.5.1.2 Lunar Crater Volcanic Field 1.5.2 HYDICE Data; 1.5.3 Hyperion Data; 1.6 Synthetic Images to Be Used in this Book; 1.7 How to Use this Book; 1.8 Notations and Terminology Used in the Book; Part I: Fundamentals; Chapter 2: Simplex Volume Calculation; 2.1 Introduction; 2.2 Determinant-Based Simplex Volume Calculation; 2.3 Geometric Simplex Volume Calculation; 2.4 General Theorem for Geometric Simplex Volume Calculation; 2.5 A Mathematical Toy Example; 2.6 Real Image Experiments; 2.7 Conclusions; Chapter 3: Discrete-Time Kalman Filtering for Hyperspectral Processing; 3.1 Introduction.
505	8		_a3.2 Discrete-Time Kalman Filtering3.2.1 A Priori and A Posteriori State Estimates; 3.2.2 Finding an Optimal Kalman Gain K(k); 3.2.3 Orthogonality Principle; 3.2.4 Discrete-Time Kalman Predictor and Filter; 3.3 Kalman Filter-Based Linear Spectral Mixture Analysis; 3.4 Kalman Filter-Based Hyperspectral Signal Processing; 3.4.1 Kalman Filter-Based Hyperspectral Signal Processing; 3.4.2 Kalman Filter-Based Spectral Signature Estimator ; 3.4.3 Kalman Filter-Based Spectral Signature Identifier ; 3.4.4 Kalman Filter-Based Spectral Signature Quantifier ; 3.5 Conclusions.
505	8		_aChapter 4: Target-Specified Virtual Dimensionality for Hyperspectral Imagery4.1 Introduction; 4.2 Review of VD; 4.3 Eigen-Analysis-Based VD; 4.3.1 Binary Composite Hypothesis Testing Formulation; 4.3.1.1 HFC Method; 4.3.1.2 Maximum Orthogonal Complement Algorithm; 4.3.2 Discussions of HFC Method and MOCA; 4.4 Finding Targets of Interest; 4.4.1 What Are Targets of Interest?; 4.4.2 Second-Order-Statistics (2OS)-Specified Target VD; 4.4.2.1 OSP-Specified Targets; 4.4.2.2 Least-Squares-Specified Targets; Unsupervised Least-Squares OSP Method.
520	3		_aThis 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. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data.
650		7	_aProcesadores digitales de señal _2embne _0(OCoLC)fst01118288 _0 _9162764
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-45171-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95875 _d95875 _x1