Abstract
Two techniques for detecting point targets in hyperspectral imagery are described. The first technique is based on the principal component analysis of hyperspectral data. We combine the information of the first two principal component analysis images; the result is a single image display "summary" of the data cube. The summary frame is used to define image segments. The statistics, means and variances, of each segment for the principal component images is calculated and a covariance matrix is constructed. The local pixel statistics and the segment statistics are then used to evaluate the extent to which each pixel differs from its surroundings. Point target pixels will have abnormally high values. The second technique operates on each band of the hypercube. A local anti-median of each pixel is taken and is weighted by the standard deviation of the local neighborhood. The results of each band are then combined. Results will be shown for visible, SWIR, and MWIR hyperspectral imagery.
| Original language | English |
|---|---|
| Pages (from-to) | 242-257 |
| Number of pages | 16 |
| 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
- Hyperspectral data
- Point target detection
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering