Automatic mountain detection in lunar images using texture of DTM data

This paper presents the development and implementation of a novel approach for automatic detection of the lunar mountains using Digital Terrain Model (DTM). The approach consists preprocessing the data, denoising, extracting texture information of the DTM, choosing an appropriate threshold using the Rényi Entropy threshold selection method, then the post-processing to extract the boundary of the mountain. The approach is applied to eight test sites, which were chosen in a manner so that the mountains are isolated. The detected mountains are assessed by their morphometric properties. The accuracy of the approach was assessed by determining its accuracy in detecting manually defined mountains and by comparing diameter estimates with separately determined values. The results are in agreement with the manually detected mountains, with the average detection performance of 0.91 and average precision of 0.97. The proposed approach extracts the mountain boundaries slightly more precisely than K-Means clustering which had an average detection performance of 0.89.