Abstract
Over the last few years, we have developed an algorithm which detects anomalous targets in hyperspectral or multispectral images. The algorithm takes a data (image) cube with a completely unknown background, segments the cube, assigns the largest clusters as background, and determines which pixels are anomalous to the background. In the work to be presented here, we will add two additional modules. First, since our present mission is to detect military targets in a fairly barren rural background, we use the SAVI (or NDVI) metric to detect items which appear to contain chlorophyll. In this way, we can eliminate objects which in retrospect were the right sizes and shapes but were in reality plants. Second, we have developed CFAR methods to achieve a Constant False Alarm Rate while giving us the maximum probability of detecting the targets. Actual data will be analyzed by the algorithm; the ability to both determine if a target is present and where its location is will be shown.
Original language | English |
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Article number | 59870U |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5987 |
DOIs | |
State | Published - 1 Dec 2005 |
Event | Electro-Optical and Infrared Systems: Technology and Applications II - Bruges, Belgium Duration: 26 Sep 2005 → 27 Sep 2005 |
Keywords
- Anomaly Detection
- CFAR
- Hyperspectral Data
- Multispectral Data
- NDVI
- SAVI
- SRC
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering