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Proceedings Paper

Combining CFAR with anomaly detection at hyperspectral images
Author(s): Eran Ohel; Stanley R. Rotman; Dan G. Blumberg
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Paper Abstract

Over the last few years, we have developed an algorithm which detects anomalous targets in hyperspectral images. The algorithm takes a hyperspectral 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.

Paper Details

Date Published: 3 November 2005
PDF: 6 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604304 (3 November 2005); doi: 10.1117/12.652373
Show Author Affiliations
Eran Ohel, Ben-Gurion Univ. of the Negev (Israel)
Stanley R. Rotman, Ben-Gurion Univ. of the Negev (Israel)
Dan G. Blumberg, Ben-Gurion Univ. of the Negev (Israel)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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