Detection of strong mine presence indicators using intelligent algorithms

Suspected Hazardous Area extent is a very important task. It can be done by using advanced computer vision methods and artificial intelligence algorithms on airborne and space imagery in order to extract new information of SHA and to detect indicators of mine presence. We introduce the concept and describe a procedure of strong mine presence indicators detection by using convolutional neural networks and rule-based inference. Also, we propose a recommender system that improves detection quality with interactive relevance feedback. Such a system may also assist in post-processing procedures and classification of indicators after their detection.