| Paper: | TP-L4.1 |
| Session: | Geosciences and Remote Sensing I |
| Time: | Tuesday, September 18, 14:30 - 14:50 |
| Presentation: |
Lecture
|
| Title: |
DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGES FOR COLOR DISPLAY USING SEGMENTED INDEPENDENT COMPONENT ANALYSIS |
| Authors: |
Yingxuan Zhu; Syracuse University | | |
| | Pramod K. Varshney; Syracuse University | | |
| | Hao Chen; Syracuse University | | |
| Abstract: |
The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. Experimental results are presented to illustrate the performance of our approaches. |
