Classification of Martian Terrains via Deep Clustering of Mastcam Images

Mario Parente, Tejas Panambur

Abstract: In this work, we adapt a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features to the unsupervised training of image patches from data acquired by the mast cameras on the MSL Curiosity rover. The method iteratively groups the features with a k-means clustering algorithm, and uses the subsequent assignments as supervision to update the weights of the network. The resulting model performs reasonably according to visual inspection of the cluster quality and simple clustering performance measures. The results however highlight the need for a strong validation via expert assessment of the morphological features within the scenes.

External IDs:dblp:conf/igarss/ParenteP20