A NOVEL MINEFIELD DETECTION APPROACH BASED ON MORPHOLOGICAL DIVERSITY

Battlefield surveillance is a common application of synthetic aperture radar (SAR), in which minefield detection is a challenging task. In this paper, a novel minefield detection approach is proposed via the morphological diversities between targets and background. Firstly, SAR image speckle is suppressed effectively by total variation, and targets edges are preserved well. Secondly, a nonlinear transform is introduced to map the special distributed targets, e.g. landmines, into spot targets. Lastly, the modification of morphological component analysis is adopted to improve the signal-to-clutter ratio and separate the spot targets from image. The performance of the proposed approach is validated by using the data acquired over an airship mounted SAR system.