Clustering-based dome detection in lunar images using DTM data

Abstract

ABSTRACTAs the quest for lunar exploration is increasing, large numbers of lunar images are being acquired by many satellites. These images provide more information about topographic features on the lunar surface. Detecting and mapping these features using satellite images are of great scientific interest to understand them in detail. This article presents the development and implementation of an approach for automatic lunar domes detection from digital terrain model using clustering techniques. This approach consists of pre-processing, denoising, clustering, segmentation, post-processing, and boundary detection. This approach also examines domes morphometric property such as diameter, circularity index, and aspect ratio. The proposed method is experimented on nine test sites and evaluated by manual analysis for accurate detection with the detailed qualitative assessment. The manual analysis depicts that the results are in agreement with the automatic detection, while the overall statistical results reveal the detection performance as the true detection rate and false detection rate, which is achieved as 83.75% and 16.25%. In addition, the evaluated results also depict the morphometric parameters diameter, circularity index, and aspect ratio from the detected dome.