Robust automatic target recognition in FLIR imagery

Yusuf Soyman

doi:10.1117/12.919014

2 May 2012 Robust automatic target recognition in FLIR imagery

Yusuf Soyman

Proceedings Volume 8391, Automatic Target Recognition XXII; 839105 (2012) https://doi.org/10.1117/12.919014
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

In this paper, a robust automatic target recognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then performed on a few moments that provide effective performance. Bayes method is used for classification, using Mahalanobis distance as the Bayes' classifier. Results are assessed based on false alarm rates. The proposed method is shown to be robust against rotations, translations and scale effects. Moreover, it is shown to effectively perform under low-contrast objects in FLIR images. Performance comparisons are also performed on both GPU and CPU. Results indicate that GPU has superior performance over CPU.

Citation Download Citation

Yusuf Soyman "Robust automatic target recognition in FLIR imagery", Proc. SPIE 8391, Automatic Target Recognition XXII, 839105 (2 May 2012); https://doi.org/10.1117/12.919014

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