<title>Detection and classification of mines via discriminant features and borrowed strength</title>

Abstract

In this paper we introduce a method for detecting landmines from remotely sensed data. Our primary goal is to minimize the number of false negatives, while keeping the number of false positive very low.We concentrate on two different phases of the processing: detection and classification. We construct a feature based detector which utilizes some basic signal processing techniques to produce a detection map which has very few false negatives, and quite a few false positives. The detection phase of this paper concentrates on a background equalized matched filter, along with some measures of symmetry. After the detection phase we will construct a classifier for the problem which concentrates on estimating the density distributions of the pixel values. We will show that a variation of 'Borrowed Strength' as introduced by Priebe, significantly improves the classification process. This classification technique will reduce the number of false positives, without significantly increasing the number of false negatives. The procedure is demonstrated on passive multispectral mine data generously provided by the NSWC Dahlgren Division. Coastal Systems Station, Panama City, Florida. In conclusion, we propose a method for improving the performance of this type of classification. We believe that the proposed method shows a great deal of promise, and will be of use in many situations.