Multiple-aspect fixed-range template matching for the detection and classification of underwater unexploded ordnance in DIDSON sonar images

This paper presents a sonar specific methodology to detect and classify underwater unexploded ordnance (UXO) in the low resolution sonar data captured by the DIDSON US300. This technique, known as the Multiple-Aspect Fixed-Range Template Matching (MAFR-TM) algorithm, is designed to detect and classify a target of high characteristic impedance in an environment that contains similar shaped objects of low characteristic impedance. The MAFR-TM is based on the proven concept of template matching, which is a two-dimensional correlation between a reference image (template) and an image collected during field operations (source image). In the MAFR-TM algorithm, the template matching method is efficiently implemented in the wave number domain using two-dimensional Fast Fourier Transforms (2D-FFT) and wave number leakage is reduced with an optimized separable two-dimensional Kaiser window. Experimental results are provided to demonstrate the performance of the proposed approach.