A Kernel Machine Framework for Feature Optimization in Multi-frequency Sonar Imagery

Stack, J.R.; Arrieta, Rodolfo; Liao, Xiaozhong; Carin, Lawrence

doi:10.1109/oceans.2006.307121

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…They were determined in the analysis presented in [10]. These are optimized for a Kernel Machine type classifier according to the method developed in [11]. However, the feature thresholds were held constant for both experiments.…”

Section: Discussionmentioning

confidence: 99%

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Environmentally-adaptive automated target recognition algorithm using parametric characterization of seabed textures

Gazagnaire

Cobb

Beaujean

2010

Oceans 2010 MTS/Ieee Seattle

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Abstract-The undersea survey mission performed with a side scan sonar system can generate large volumes of data. Automating the detection and classification process using automated target recognition (ATR) is desirable to reduce post processing times and manning requirements. Traditionally ATR algorithms are trained using image exemplars representing the intended target, false targets and the expected operating environment. However, given the variability of the undersea environment, training a single ATR on all environments may degrade performance when operating in a specific environment, since operating thresholds are fixed. In this paper, we propose an ATR algorithm that changes its operating thresholds based on seabed texture parameter estimates of the sonar image statistics. A large set of sonar images with targets inserted at various ranges and orientations against various backgrounds was synthesized. The environmentally-adaptive ATR algorithm was trained on these specific environments and the optimal classification thresholds were stored in a look-up table (LUT). Upon encountering a novel test pattern, the environmental parameters of the sonar image texture are estimated and then used to index the LUT for the appropriate ATR operating threshold. Using this methodology, we show a performance increase for an adaptive ATR that encounters variable seabed environments versus an ATR algorithm with fixed operating thresholds trained with sonar images representative of a wide variety of environments.

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Section: Discussionmentioning

confidence: 99%

“…This knowledge is used to update the matched filter shadow region as the range to the target changes. The feature thresholds η 1 , η 2 , and η 3 were determined using the optimization methods described in [11] and implemented in [10].…”

Section: A Simulated Imagesmentioning

confidence: 99%