Improved Self-Supervised Texture Recognition of Mastcam Images by Eliminating Mixed Terrain and Range Patches

Tejas Panambur, Mario Parente

Abstract: Homogeneity within a terrain image is crucial for the scientific categorization of the image. Images consisting of more than one terrain class are irrelevant to geologic tasks such as classification, and novelty detection which require granular terrain categories. Further, images containing far-away geological objects are less relevant for scientists interested in studying rock types for Martian paleoclimate and habitability. In this work, we use image segmentation to identify and eliminate images that show multiple terrains, and depth estimation to exclude images taken from a distance beyond a certain range. We then show an improvement in the performance of deep clustering of Martian terrain images qualitatively and discuss the resulting retrieval performance that helps scientists rapidly categorize geologic terrain images.

External IDs:dblp:conf/igarss/PanamburP23