Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

1. Front Matter

   Pages I-XV

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2. Introduction

   1. Introduction

      • Pramod K. Varshney, Manoj K. Arora

      Pages 1-8

3. General

   1. Front Matter

      Pages 9-9

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   2. Hyperspectral Sensors and Applications

      • Richard Lucas, Aled Rowlands, Olaf Niemann, Ray Merton

      Pages 11-49

   3. Overview of Image Processing

      • Raghuveer M. Rao, Manoj K. Arora

      Pages 51-85

4. Theory

   1. Front Matter

      Pages 87-87

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   2. Mutual Information: A Similarity Measure for Intensity Based Image Registration

      • Hua-mei Chen

      Pages 89-108

   3. Independent Component Analysis

      • Stefan A. Robila

      Pages 109-132

   4. Support Vector Machines

      • Mahesh Pal, Pakorn Watanachaturaporn

      Pages 133-157

   5. Markov Random Field Models

      • Teerasit Kasetkasem

      Pages 159-178

5. Applications

   1. Front Matter

      Pages 179-179

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   2. MI Based Registration of Multi-Sensor and Multi-Temporal Images

      • Hua-mei Chen, Pramod K. Varshney

      Pages 181-198

   3. Feature Extraction from Hyperspectral Data Using ICA

      • Stefan A. Robila, Pramod K. Varshney

      Pages 199-216

   4. Hyperspectral Classification Using ICA Based Mixture Model

      • Chintan A. Shah

      Pages 217-236

   5. Support Vector Machines for Classification of Multi- and Hyperspectral Data

      • Manoj K. Arora, Pakorn Watanachaturaporn

      Pages 237-255

   6. An MRF Model Based Approach for Sub-pixel Mapping from Hyperspectral Data

      • Teerasit Kasetkasem, Manoj K. Arora, Pramod K. Varshney

      Pages 257-277

   7. Image Change Detection and Fusion Using MRF Models

      • Teerasit Kasetkasem, Pramod K. Varshney

      Pages 279-307

6. Back Matter

   Pages 309-323

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Over the last fifty years, a large number of spaceborne and airborne sensors have been employed to gather information regarding the earth's surface and environment. As sensor technology continues to advance, remote sensing data with improved temporal, spectral, and spatial resolution is becoming more readily available. This widespread availability of enormous amounts of data has necessitated the development of efficient data processing techniques for a wide variety of applications. In particular, great strides have been made in the development of digital image processing techniques for remote sensing data. The goal has been efficient handling of vast amounts of data, fusion of data from diverse sensors, classification for image interpretation, and development of user-friendly products that allow rich visualization. This book presents some new algorithms that have been developed for high­ dimensional datasets, such as multispectral and hyperspectral imagery. The contents of the book are based primarily on research carried out by some members and alumni of the Sensor Fusion Laboratory at Syracuse University.

• Digital Image Processing
• GIS
• Hyperspectral Imaging
• Markov Random Field
• Pattern Recognition and Data Fusion
• Remote Sensing
• image processing
• image registration

• Department of Electrical Engineering and Computer Science, 121 Link Hall, Syracuse University, Syracuse, USA

  Pramod K. Varshney

• Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India

  Manoj K. Arora

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