Abstract
Further refinements are presented on a simple and fast way to cluster/segment hyperspectral imagery. In earlier work, it was shown that, starting with the first 2 principal component images, one could form a 2-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks. Issues that we analyzed this year are the proper weighting of the different principal components as a function of the peak shape and automatic methods based on an entropy measure to control the number of clusters and the segmentation of the data to produce the most meaningful results. Examples from both visible and infrared hyperspectral data will be shown.
| Original language | English |
|---|---|
| Pages (from-to) | 270-277 |
| Number of pages | 8 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 4816 |
| DOIs | |
| State | Published - 1 Dec 2002 |
| Event | Imaging Spectrometry VII - Seattle, WA, United States Duration: 8 Jul 2002 → 10 Jul 2002 |
Keywords
- Entropy
- Histograms
- Hyperspectral
- Principal components
- Segmentation
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering