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Hyperspectral Imaging

Techniques for Spectral Detection and Classification

  • Book
  • © 2003

Overview

Authors:
  1. Chein-I Chang

    1. Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, USA

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    1. Introduction

      • Chein-I Chang
      Pages 1-11

  3. Hyperspectral Measures

    1. Front Matter

      Pages 13-13
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    2. Hyperspectral Measures for Spectral Characterization

      • Chein-I Chang
      Pages 15-35

  5. Unconstrained Mixed Pixel Classification

    1. Front Matter

      Pages 139-140
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    2. Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projection

      • Chein-I Chang
      Pages 141-159

    3. A Quantitative Analysis of Mixed-to-Pure Pixel Conversion (MPCV)

      • Chein-I Chang
      Pages 161-178

  7. Automatic Mixed Pixel Classification (AMPC)

    1. Front Matter

      Pages 243-244
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    2. Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification

      • Chein-I Chang
      Pages 245-255

    3. Automatic Mixed Pixel Classificatio (AMPC): Anomaly Classification

      • Chein-I Chang
      Pages 257-275
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Keywords

About this book

Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

Authors and Affiliations

  • Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, USA

    Chein-I Chang

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