HAMD: a software system for surface and buried mine detections

Abstract

We present an algorithm suite, Hybrid Airborne Mine Detector (HAMD), developed for the detection of small scatterable surface and buried mines, using multispectral airborne images. This algorithm suite is composed of a number of components designed for specific tasks such as image segmentation based on unsupervised clustering, localized image enhancement, generalized signature extraction and construction, and mine classification and fusion. Since both surface and buried mines in low contrast images are difficult to detect, a new algorithm has been developed to enhance images locally. The signature extraction component extracts different signatures based on surface or buried mines. To extract small surface mine signatures, moment invariance (MI) is used. However, to extract buried mine signatures, thermal variations and spatial distributions are employed. To make the system suitable for different operational environments, a small number of general signatures are constructed and stored in the signature library. Test results based on airborne images have shown that signatures collected can be used to detect mines placed in different environments such as vegetation and sandy areas. For mine classification and false alarm mitigation, statistical hypothesis tests, such as Fisher's Discriminant Ratio (FDR) test and the Kolmogorov-Smirnov (KS) test, are used.

Real-time buried threat detection and cueing capability in VPEF environment

“…In this paper, we present the architecture of this vehicular buried threat detection approach developed over the past several years ( [1], [2], [3] and [4]), and its latest implementation and integration in VPEF (Video Processing and Exploitation Framework) environment. During the buried threat emplacement, the surface soils are inevitably disturbed, resulting in different heat absorption and dissipation rate as compared to the surrounding soils.…”