Information content vs. class separabilityat optimal spectral regions

Conference Paper (2017)
Author(s)

SE Aria (TU Delft - Optical and Laser Remote Sensing)

Massimo Menenti (TU Delft - Optical and Laser Remote Sensing)

Ben Gorte (TU Delft - Optical and Laser Remote Sensing)

Research Group
Optical and Laser Remote Sensing
Copyright
© 2017 S.E. Hosseini Aria, M. Menenti, B.G.H. Gorte
DOI related publication
https://doi.org/10.1109/WHISPERS.2013.8080747
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 S.E. Hosseini Aria, M. Menenti, B.G.H. Gorte
Research Group
Optical and Laser Remote Sensing
Volume number
2013-June
ISBN (electronic)
9781509011193
Reuse Rights

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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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