LEADER 06127nam 2200709 a 450 001 9910141611003321 005 20240516100621.0 010 $a1-118-26978-0 010 $a1-118-26977-2 010 $a1-299-24186-7 010 $a1-118-26975-6 035 $a(CKB)2670000000336195 035 $a(EBL)832589 035 $a(SSID)ssj0000831930 035 $a(PQKBManifestationID)11530822 035 $a(PQKBTitleCode)TC0000831930 035 $a(PQKBWorkID)10880804 035 $a(PQKB)10443775 035 $a(Au-PeEL)EBL832589 035 $a(CaPaEBR)ebr10662606 035 $a(CaONFJC)MIL455436 035 $a(CaSebORM)9781118269770 035 $a(MiAaPQ)EBC832589 035 $a(OCoLC)833047806 035 $a(EXLCZ)992670000000336195 100 $a20111019d2013 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHyperspectral data processing $ealgorithm design and analysis /$fChein-I Chang 205 $a1st ed. 210 $aHoboken, N.J. $cWiley-Interscience$d2013 215 $a1 online resource (1165 p.) 300 $aDescription based upon print version of record. 311 $a0-471-69056-2 320 $aIncludes bibliographical references and index. 327 $aHYPERSPECTRAL DATA PROCESSING: Algorithm Design and Analysis; CONTENTS; PREFACE; 1 OVERVIEW AND INTRODUCTION; 1.1 Overview; 1.2 Issues of Multispectral and Hyperspectral Imageries; 1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery; 1.3.1 Misconception: Hyperspectral Imaging is a Natural Extension of Multispectral Imaging; 1.3.2 Pigeon-Hole Principle: Natural Interpretation of Hyperspectral Imaging; 1.4 Scope of This Book; 1.5 Book's Organization; 1.5.1 Part I: Preliminaries; 1.5.2 Part II: Endmember Extraction; 1.5.3 Part III: Supervised Linear Hyperspectral Mixture Analysis 327 $a1.5.4 Part IV: Unsupervised Hyperspectral Analysis 1.5.5 Part V: Hyperspectral Information Compression; 1.5.6 Part VI: Hyperspectral Signal Coding; 1.5.7 Part VII: Hyperspectral Signal Feature Characterization; 1.5.8 Applications; 1.5.8.1 Chapter 30: Applications of Target Detection; 1.5.8.2 Chapter 31: Nonlinear Dimensionality Expansion to Multispectral Imagery; 1.5.8.3 Chapter 32: Multispectral Magnetic Resonance Imaging; 1.6 Laboratory Data to be Used in This Book; 1.6.1 Laboratory Data; 1.6.2 Cuprite Data; 1.6.3 NIST/EPA Gas-Phase Infrared Database 327 $a1.7 Real Hyperspectral Images to be Used in this Book 1.7.1 AVIRIS Data; 1.7.1.1 Cuprite Data; 1.7.1.2 Purdue's Indiana Indian Pine Test Site; 1.7.2 HYDICE Data; 1.8 Notations and Terminologies to be Used in this Book; I: PRELIMINARIES; 2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES; 2.1 Introduction; 2.2 Subsample Analysis; 2.2.1 Pure-Sample Target Detection; 2.2.2 Subsample Target Detection; 2.2.2.1 Adaptive Matched Detector (AMD); 2.2.2.2 Adaptive Subspace Detector (ASD); 2.2.3 Subsample Target Detection: Constrained Energy Minimization (CEM); 2.3 Mixed Sample Analysis 327 $a2.3.1 Classification with Hard Decisions 2.3.1.1 Fisher's Linear Discriminant Analysis (FLDA); 2.3.1.2 Support Vector Machines (SVM); 2.3.2 Classification with Soft Decisions; 2.3.2.1 Orthogonal Subspace Projection (OSP); 2.3.2.2 Target-Constrained Interference-Minimized Filter (TCIMF); 2.4 Kernel-Based Classification; 2.4.1 Kernel Trick Used in Kernel-Based Methods; 2.4.2 Kernel-Based Fisher's Linear Discriminant Analysis (KFLDA); 2.4.3 Kernel Support Vector Machine (K-SVM); 2.5 Conclusions; 3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS; 3.1 Introduction 327 $a3.2 Neyman-Pearson Detection Problem Formulation 3.3 ROC Analysis; 3.4 3D ROC Analysis; 3.5 Real Data-Based ROC Analysis; 3.5.1 How to Generate ROC Curves from Real Data; 3.5.2 How to Generate Gaussian-Fitted ROC Curves; 3.5.3 How to Generate 3D ROC Curves; 3.5.4 How to Generate 3D ROC Curves for Multiple Signal Detection and Classification; 3.6 Examples; 3.6.1 Hyperspectral Imaging; 3.6.1.1 Hyperspectral Target Detection; 3.6.1.2 Linear Hyperspectral Mixture Analysis; 3.6.2 Magnetic Resonance (MR) Breast Imaging; 3.6.2.1 Breast Tumor Detection; 3.6.2.2 Brain Tissue Classification 327 $a3.6.3 Chemical/Biological Agent Detection 330 $a"This book is intended to be a sequel from the author's other title with Kluwer "Hyperspectral Imaging: Techniques for Spectral Detection and Classification". It contains five major parts. Part I is new aspects of OSP including 7 chapters, OSP revisit, generalized OSP, FPGA designs for OSP and CEM, Kalman filter-based linear unmixing, least squares fully constrained linear mixture analysis, exploitation-based hyperspectral data compression and size estimation of supixel targets, Part II is interference rejection for linear unmixing composed of three chapters, signal-composed interference-annihilated theory, interference-annihilated noise-adjusted theory and information-processed matched filter theory; Part III is nonlinear non-literal techniques for linear unmixing consisting of 3 chapters, convex cone analysis, information theoretic criterion-based project pursuit and nonlinear mixing model analysis; Part IV is spectral coding comprising of three chapters, progressive spectral coding, spectral binary coding and spectral coding for band selection; Part V is applications made up of two chapters, applications to magnetic resonance imaging and landmine detection"--$cProvided by publisher. 606 $aImage processing$xDigital techniques 606 $aSpectroscopic imaging 606 $aSignal processing 615 0$aImage processing$xDigital techniques. 615 0$aSpectroscopic imaging. 615 0$aSignal processing. 676 $a621.39/94 686 $aTEC015000$2bisacsh 700 $aChang$b Chein-I$0763028 701 $aChang$b Chein-I$0763028 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141611003321 996 $aHyperspectral data processing$92238654 997 $aUNINA