Information content vs. class separabilityat optimal spectral regions

Conference paper (2017)

Authors

S.E. Hosseini Aria Optical and Laser Remote Sensing -

M. Menenti Optical and Laser Remote Sensing -

B.G.H. Gorte Optical and Laser Remote Sensing -

Research Group

Optical and Laser Remote Sensing () (TU Delft)

DOI: https://doi.org/10.1109/WHISPERS.2013.8080747

Separability Information content Hyperspectral Spectral region

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Published Date

23-10-2017

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Optical and Laser Remote Sensing

Abstract

One of the main steps in hyperspectral image classification is the selection of bands that provide the best separability among classes. It is usually understood that the selected bands for classification must contain a large amount of information, and the correlation among selected bands should be small to avoid redundancy. At the same time for optimal classification, class separability should be at maximum value. The question arises whether the most informative spectral regions are really the same as the most discriminant ones for a given set of classes. Answering the question, we developed a new method named Spectral Region Splitting (SRS) to identify interesting spectral regions. This article concludes that the optimal informative and the optimal separable spectral regions are not identical. Furthermore, the cause of the difference is proven theoretically.

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