Buried Mine Detection in Airborne Imagery using Co-Occurrence Texture Features
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 uncorrected 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-à-vis multi-band RX anomaly detection, and validate the proposed clustering-Based detection method.
Recommended Citation
S. Tiwari et al., "Buried Mine Detection in Airborne Imagery using Co-Occurrence Texture Features," Proceedings of SPIE - The International Society for Optical Engineering, vol. 6553, article no. 65530Z, Society of Photo-optical Instrumentation Engineers, Nov 2007.
The definitive version is available at https://doi.org/10.1117/12.721509
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Buried mine detection; Cross- Co-occurrence matrix features; False alarm mitigation; Feature selection; Gray-scale co-occurrence matrix features; Principal feature analysis; Texture features
International Standard Book Number (ISBN)
978-081946675-4
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
15 Nov 2007
