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.
Original language | English |
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Title of host publication | 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013 |
Publisher | IEEE |
Number of pages | 4 |
Volume | 2013-June |
ISBN (Electronic) | 9781509011193 |
DOIs | |
Publication status | Published - 23 Oct 2017 |
Event | 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Gainesville, United States Duration: 26 Jun 2013 → 28 Jun 2013 Conference number: 5 |
Conference
Conference | 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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Abbreviated title | WHISPERS 2013 |
Country/Territory | United States |
City | Gainesville |
Period | 26/06/13 → 28/06/13 |
Keywords
- Hyperspectral
- Information content
- Separability
- Spectral region