Most of the image processing techniques for multispectral or hyperspectral data have complexity that depends directly on the number of spectral bands in the acquired data (Swain and Davis 1978). Due to the large number of bands involved in the hyperspectral images, it is of interest to find methods that transform the image cube into one with reduced dimensionality while, at the same time, maintaining as much information content as possible. These techniques are known under the general name of feature extraction (Richards and Jia 1999). The term feature is used to refer to the spectral bands or other transforms derived from combinations of bands.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- Feature Extraction from Hyperspectral Data Using ICA
Stefan A. Robila
Pramod K. Varshney
- Springer Berlin Heidelberg
- Chapter 8
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