Robust Automatic Target Recognition in FLIR imagery

Date

2012-04-24

Author

Soyman, Yusuf

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Gabor wavelet representation, Segmentation, Feature extraction, Classification, Automatic target recognition, Forward-looking infrared (FLIR) imagery, GPGPU, CUDA

URI

https://hdl.handle.net/11511/64326

DOI

https://doi.org/10.1117/12.919014

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. Soyman, “Robust Automatic Target Recognition in FLIR imagery,” 2012, vol. 8391, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64326.