Buried mine detection in airborne imagery using co-occurrence texture features

Spandan Tiwari; Sanjeev Agarwal; Anh Trang

doi:10.1117/12.721509

27 April 2007 Buried mine detection in airborne imagery using co-occurrence texture features

Spandan Tiwari, Sanjeev Agarwal, Anh Trang

Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII; 65530Z (2007) https://doi.org/10.1117/12.721509
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

In recent years, airborne minefield detection has increasingly been explored due to its capability for low-risk standoff detection and quick turnaround time. Significant research efforts have focused on the detection of surface mines and few techniques have been proposed specifically for buried mine detection. The detection performance of current detectors, like RX, for buried mines is not satisfactory. In this paper, we explore a methodology for buried mine detection in multi-spectral imagery, based on texture information of the target signature. A systematic approach for the selection of co-occurrence texture features is presented. Bhattacharya coefficient is used for the initial selection of discriminatory texture features, followed by principal feature analysis of the selected features, to identify minimum number of features with mutually uncorrelated information. Finally, a detection method based on unsupervised clustering of mine features in the reduced feature space, is employed for generating the test statistic for detection. Because the proposed method is based on co-occurrence matrix features, it is largely invariant to illumination changes in the images. Results for the proposed method are presented, which show improvement in the detection performance vis-a-vis multi-band RX anomaly detection, and validate the proposed clustering-based detection method.

Citation Download Citation

Spandan Tiwari, Sanjeev Agarwal, and Anh Trang "Buried mine detection in airborne imagery using co-occurrence texture features", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530Z (27 April 2007); https://doi.org/10.1117/12.721509

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