Research over the last 9 years has resulted in an effective mine classification approach that involves the use of image-segmentation based screening methods followed by multilayer perceptron networks for mine classification. The present approach centers around a baseline 23 Feature set related to highlight, shadow, and highlight/shadow contrast statistic based segmentations, and the use of associated statistical and shape related factors. In the work described here we investigate the improvement of baseline performance by incorporating image texture related features such as Cooccurrence Matrix related factors.
