Optical Remote Sensing

Advances in Signal Processing and Exploitation Techniques

  • Saurabh Prasad
  • Lori M. Bruce
  • Jocelyn Chanussot

Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Saurabh Prasad, Lori M. Bruce, Jocelyn Chanussot
    Pages 1-8
  3. Emmanuel Christophe
    Pages 9-29
  4. Robert Muise, Abhijit Mahalanobis
    Pages 49-64
  5. Maya R. Gupta, Nasiha Hrustemovic
    Pages 65-79
  6. Mauro Dalla Mura, Jon Atli Benediktsson, Jocelyn Chanussot, Lorenzo Bruzzone
    Pages 123-146
  7. Luis Gómez-Chova, Jordi Muñoz-Marí, Valero Laparra, Jesús Malo-López, Gustavo Camps-Valls
    Pages 171-206
  8. Melba M. Crawford, Li Ma, Wonkook Kim
    Pages 207-234
  9. Antonio Plaza, Gabriel Martín, Javier Plaza, Maciel Zortea, Sergio Sánchez
    Pages 235-267
  10. Lorenzo Bruzzone, Silvia Marchesi, Francesca Bovolo
    Pages 269-299

About this book


Optical remote sensing involves acquisition and analysis of optical data – electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground.  Optical image acquisition modalities have come a long way – from gray-scale photogrammetric images to hyperspectral images. The advances in imaging hardware over recent decades have enabled availability of high spatial, spectral and temporal resolution imagery to the remote sensing analyst. These advances have created unique challenges for researchers in the remote sensing community working on algorithms for representation, exploitation and analysis of such data.

Early optical remote sensing systems relied on multispectral sensors, which are characterized by a small number of wide spectral bands. Although multispectral sensors are still employed by analysts, in recent years, the remote sensing community has seen a steady shift to hyperspectral sensors, which are characterized by hundreds of fine resolution co-registered spectral bands, as the dominant optical sensing technology. Such data has the potential to reveal the underlying phenomenology as described by spectral characteristics accurately. This “extension” from multispectral to hyperspectral imaging does not imply that the signal processing and exploitation techniques can be simply scaled up to accommodate the extra dimensions in the data. This book presents state-of-the-art signal processing and exploitation algorithms that address three key challenges within the context of modern optical remote sensing: (1) Representation and visualization of high dimensional data for efficient and reliable transmission, storage and interpretation; (2) Statistical pattern classification for robust land-cover-classification, target recognition and pixel unmixing; (3) Fusion of multi-sensor data to effectively exploit multiple sources of information for analysis.


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

Editors and affiliations

  • Saurabh Prasad
    • 1
  • Lori M. Bruce
    • 2
  • Jocelyn Chanussot
    • 3
  1. 1.Geosystems Research Inst., Dept. Electrical & Computer EngineeringMississippi State UniversityMississippi StateUSA
  2. 2.Geosystems Research Inst., Dept. Electrical & Computer EngineeringMississippi State UniversityMississippi StateUSA
  3. 3.Institut Polytechnique de GrenobleGrenoble CX 1France

Bibliographic information