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Proceedings Paper

Target identification using Zernike moments and neural networks
Author(s): Mahmood R. Azimi-Sadjadi; Arta A. Jamshidi; Andrew J. Nevis
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Paper Abstract

The development of an underwater target identification algorithm capable of identifying various types of underwater targets, such as mines, under different environmental conditions pose many technical problems. Some of the contributing factors are: targets have diverse sizes, shapes and reflectivity properties. Target emplacement environment is variable; targets may be proud or partially buried. Environmental properties vary significantly from one location to another. Bottom features such as sand, rocks, corals, and vegetation can conceal a target whether it is partially buried or proud. Competing clutter with responses that closely resemble those of the targets may lead to false positives. All the problems mentioned above contribute to overly difficult and challenging conditions that could lead to unreliable algorithm performance with existing methods. In this paper, we developed and tested a shape-dependent feature extraction scheme that provides features invariant to rotation, size scaling and translation; properties that are extremely useful for any target classification problem. The developed schemes were tested on an electro-optical imagery data set collected under different environmental conditions with variable background, range and target types. The electro-optic data set was collected using a Laser Line Scan (LLS) sensor by the Coastal Systems Station (CSS), located in Panama City, Florida. The performance of the developed scheme and its robustness to distortion, rotation, scaling and translation was also studied.

Paper Details

Date Published: 22 October 2001
PDF: 8 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445360
Show Author Affiliations
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Arta A. Jamshidi, Colorado State Univ. (United States)
Andrew J. Nevis, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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