Real-time recursive hyperspectral sample and band processing : algorithm architecture and implementation /

Real-time recursive hyperspectral sample and band processing : algorithm architecture and implementation /

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Bibliographic Details
Author / Creator:Chang, Chein-I, author.
Imprint:Cham, Switzerland : Springer, 2017.
Description:1 online resource (xxiii, 690 pages) : illustrations (some color)
Language:English
Subject:
Signal processing -- Digital techniques -- Data processing.
Signal processing -- Digital techniques -- Mathematics.
Signal processing -- Digital techniques -- Data processing.
Signal processing -- Digital techniques -- Mathematics.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11273430
Hidden Bibliographic Details
ISBN:9783319451718
3319451715
9783319451701
3319451707
Digital file characteristics:text file
PDF
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed May 8, 2017).
Summary:This 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.
Other form:Print version: Chang, Chein-I. Real-time recursive hyperspectral sample and band processing. Cham, Switzerland : Springer, 2017 3319451707 9783319451701
Standard no.:10.1007/978-3-319-45171-8
Table of Contents:
  • Preface; 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.
  • 1.3.3.2 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.
  • 1.5.1.2 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.
  • 3.2 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.
  • Chapter 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.

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