Analysis of Hyperspectral Remote Sensing Images

  • Liangpei Zhang
  • Yanfei Zhong
Chapter
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Abstract

Hyperspectral remote sensing, or known as imaging spectroscopy, is a recently developed technique since the last two decades of the 20th century (Chang 2003). Imaging spectroscopy is a relatively fully-fledged experimental tool that has been successfully used in the laboratory by physicists and chemists for over 100 years for identification of materials and their composition. Absorption features accord to the special chemical bound of a material, which can be calculated by imaging spectroscopy. With the demand of earth observation, imaging spectroscopy technique has extended to the detection and mapping of materials by satellite imagery. Since the 1980s, geologists have used sensors on the man-made satellite to obtain the spectrum of every position in a large scale in the ground which combines a datacube which combines the imaging and spectroscopy in a single system. That's to say that hyperspectral remote sensing not only contain spatial features but also spectral features of the ground objects. But it doesn't refer to the remote sensing imagery with only several bands, such as Landsat TM or Modis imagery. In fact, the most significant difference between hyperspectral remote sensing and these multispectral remote sensing is that it has much more bands with much higher spectral resolution. Hyperspectral remote sensing usually has over one hundred bands with a spectral resolution of under 10 nm. Fig. 9.1 shows the concept about hyperspectral remote sensing imagery which usually comprises of datacube with a series of images. In this case, it provides a better discrimination among similar targets. On the other hand, subtle spectral differences would be hidden in spectra acquired with multispectral remote sensing with broad spectral band sensors. Hyperspectral remote sensing has been widely used in many civil and military applications such as geology, agriculture, and global change, defense, intelligence, and law enforcement. The aim of the chapter is to discuss the basic data processing and analysis techniques for hyperspectral remote sensing such as feature selection, classification, mixed pixel unmixing etc.

Keywords

Feature Selection Feature Subset Artificial Immune System Spectral Angle Mapper Antibody Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Liangpei Zhang
    • 1
  • Yanfei Zhong
    • 1
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhanChina, People’s Republic

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