Abstract
Though the complex-valued self-organizing map (CSOM) is powerful in distinction between plastic landmines and other objects in landmine visualization systems, the distinction sometimes fails in a portable low-resolution system. In this paper, we propose two techniques to enhance the visualization ability. One is the utilization of SOM-space topology in the CSOM adaptive classification. The other is a novel feature extraction method paying attention to local correlation in the frequency domain. In experimental results, we find that these two techniques significantly improve the visualization performance. The localcorrelation method contributes also make the system free from choosing an optimal "base frequency".
