29 April 2008 HAMD: a software system for surface and buried mine detections
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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.
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Bo Ling, Ming Liu, Uma Venkataraman, Anh H. Trang, "HAMD: a software system for surface and buried mine detections", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695318 (29 April 2008); doi: 10.1117/12.780369; https://doi.org/10.1117/12.780369
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KEYWORDS
Land mines

Image segmentation

Mining

Detection and tracking algorithms

Image enhancement

Vegetation

Image processing algorithms and systems

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