Optical Remote Sensing

1. Front Matter

   Pages i-viii

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2. Introduction

   • Saurabh Prasad, Lori M. Bruce, Jocelyn Chanussot

   Pages 1-8

3. Hyperspectral Data Compression Tradeoff

   • Emmanuel Christophe

   Pages 9-29

4. Reconstructions from Compressive Random Projections of Hyperspectral Imagery

   • James E. Fowler, Qian Du

   Pages 31-48

5. Integrated Sensing and Processing for Hyperspectral Imagery

   • Robert Muise, Abhijit Mahalanobis

   Pages 49-64

6. Color Science and Engineering for the Display of Remote Sensing Images

   • Maya R. Gupta, Nasiha Hrustemovic

   Pages 65-79

7. An Evaluation of Visualization Techniques for Remotely Sensed Hyperspectral Imagery

   • Shangshu Cai, Robert Moorhead, Qian Du

   Pages 81-98

8. A Divide-and-Conquer Paradigm for Hyperspectral Classification and Target Recognition

   • Saurabh Prasad, Lori M. Bruce

   Pages 99-122

9. The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images

   • Mauro Dalla Mura, Jon Atli Benediktsson, Jocelyn Chanussot, Lorenzo Bruzzone

   Pages 123-146

10. Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Means of Hyperspectral Data

    • Karoly Livius Bakos, Prashanth Reddy Marpu, Paolo Gamba

    Pages 147-170

11. A Review of Kernel Methods in Remote Sensing Data Analysis

    • Luis Gómez-Chova, Jordi Muñoz-Marí, Valero Laparra, Jesús Malo-López, Gustavo Camps-Valls

    Pages 171-206

12. Exploring Nonlinear Manifold Learning for Classification of Hyperspectral Data

    • Melba M. Crawford, Li Ma, Wonkook Kim

    Pages 207-234

13. Recent Developments in Endmember Extraction and Spectral Unmixing

    • Antonio Plaza, Gabriel Martín, Javier Plaza, Maciel Zortea, Sergio Sánchez

    Pages 235-267

14. Change Detection in VHR Multispectral Images: Estimation and Reduction of Registration Noise Effects

    • Lorenzo Bruzzone, Silvia Marchesi, Francesca Bovolo

    Pages 269-299

15. Effects of the Spatial Enhancement of Hyperspectral Images on the Distribution of Spectral Classes

    • Andrea Garzelli, Luca Capobianco

    Pages 301-327

16. Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings

    • Diego Polli, Fabio Dell’Acqua

    Pages 329-341

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data. Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.

• high spectral resolution
• hyperspectral imagery possessing
• multispectral imagery possessing
• pre-processing images

From the reviews:

“This book is composed of contributions that address many of the contemporary problems faced when processing hyperspectral image data. As expected, there are chapters focussed on thematic mapping and classification, spectral un-mixing, morphology and compression … . important to anyone interested in the state of kernel based methods in image analysis. … It is very well written, makes very good use of examples and will be an important reference work for those working on un-mixing problems.” (John Richards, IEEE Geoscience and Remote Sensing Society Newsletter, June, 2011)

“This excellent reference focuses on advances in signal processing and exploitation techniques for optical remote sensing with a collection of state-of-the art algorithms for hyperspectral and multispectral imaging technologies. It is intended for advanced users, particularly graduate students and image scientists specializing in the field of optical remote sensing. … This cutting-edge publication includes a collection of images and graphics, processing technologies and parallel implementations with up-to-date references at the end of each chapter.” (Axel Mainzer Koenig, Optics & Photonics News, July, 2012)

• Geosystems Research Inst., Dept. Electrical & Computer Engineering, Mississippi State University, Mississippi State, USA

  Saurabh Prasad, Lori M. Bruce

• Institut Polytechnique de Grenoble, Grenoble CX 1, France

  Jocelyn Chanussot

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