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Proceedings Paper

Detection of minelike targets in heavily cluttered environments using the MNF transform and grayscale morphological image reconstruction
Author(s): Ashish Banerji; John Ioannis Goutsias
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Paper Abstract

We consider the problem of detecting minelike targets, imaged by means of multispectral sensors, that have been heavily corrupted by clutter. An effective detection approach needs to take into consideration the high correlation that is often present among bands in multispectral images and be robust against clutter. To this end, we here propose a two-step target detection approach. In particular, we first employ the Maximum Noise Fraction transform, in conjunction with vector-morphology, in order to reduce the effect of clutter and enhance the presence of targets. We then discuss a target detection algorithm, based on a morphological image reconstruction/marker fusion approach. We apply this algorithm to the problem of detecting minelike targets present in six-band aerial images, provided to us by the Coastal Systems Station, Naval Surface Warfare Center, Panama City, Florida. The proposed technique is relatively simple and requires only approximate knowledge of target size.

Paper Details

Date Published: 31 May 1996
PDF: 11 pages
Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); doi: 10.1117/12.241264
Show Author Affiliations
Ashish Banerji, Johns Hopkins Univ. (United States)
John Ioannis Goutsias, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 2765:
Detection and Remediation Technologies for Mines and Minelike Targets
Abinash C. Dubey; Robert L. Barnard; Colin J. Lowe; John E. McFee, Editor(s)

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