Image characterization and target recognition in the surf zone environment

Andrew J. Nevis

doi:10.1117/12.241263

31 May 1996 Image characterization and target recognition in the surf zone environment

Andrew J. Nevis

Proceedings Volume 2765, Detection and Remediation Technologies for Mines and Minelike Targets; (1996) https://doi.org/10.1117/12.241263
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

The surf zone environment represents a very difficult challenge for electro-optic surveillance programs. Data from these programs have been shown to contain dense clutter from vegetation, biological factors (fish), and man-made objects, and is further complicated by the water to land transition which has a significant impact on target signal-to-noise ratios (SNR). Also, targets can be geometrically warped from the sea surface and by occlusion from sand and breaking waves. The Program Executive Office Mine Warfare (PMO-210) recently sponsored a test under the Magic Lantern Adaptation (MLA) program to collect surf zone data. Analysis of the data revealed a dilemma for automatic target recognition algorithms; threshold target features high enough to reduce high false alarm rates from land clutter or low enough to detect and classify underwater targets. Land image typically have high SNR clutter with crisp edges while underwater images have lower SNR clutter with blurred edges. In an attempt to help distinguish between land and underwater images, target feature thresholds were made to vary as a function of the SNR of image features within images and as a function of a measure of the edge crispness of the image features. The feasibility of varying target feature thresholds to reduce false alarm rates was demonstrated on a target recognition program using a small set of MLA data. Four features were developed based on expected target shape and resolution: a contrast difference measure between circular targets and their local backgrounds, a signal-to-noise ratio, a normalized correlation, and a target circularity measure. Results showed a target probability of detection and classification (P_dc) of 50 - 78% with false alarms per frame of less than 4%.

Citation Download Citation

Andrew J. Nevis "Image characterization and target recognition in the surf zone environment", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241263

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