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In this paper, we consider the application of texture features for antitank landmine detection in ground- penetrating-radar data in the difficult scenario of very high clutter environments. In particular, we develop a technique for 3-D texture feature extraction, and we compare the results for landmine/clutter discrimination using classifiers that are built on 3-D as well as on 2-D texture feature sets. Our results indicate performance improvements across several different challenging testing scenarios when using the relevance-vector-machine classifiers that are trained on our 3-D feature sets as compared to the performance using the 2-D texture feature sets.
Geoscience and Remote Sensing, IEEE Transactions on (Volume:45 , Issue: 7 )
Date of Publication: July 2007