Mapping “Brain Terrain” Regions on Mars Using Deep Learning

Abstract

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been focusing on regions that may have liquid or frozen water. A set of critical areas may have seen cycles of ice thawing in the relatively recent past in response to periodic changes in the obliquity of Mars. In this work, we use convolutional neural networks to detect surface regions containing “brain terrain,” a landform on Mars whose similarity in morphology and scale to sorted stone circles on Earth suggests that it may have formed as a consequence of freeze/thaw cycles. We use large images (∼100–1000 megapixels) from the Mars Reconnaissance Orbiter to search for these landforms at resolutions close to a few tens of centimeters per pixel (∼25–50 cm). Over 58,000 images (∼28 TB) were searched (∼5% of the Martian surface), and we found detections in 201 images. To expedite the processing, we leverage a classifier network (prior to segmentation) in the Fourier domain that can take advantage of JPEG compression by leveraging blocks of coefficients from a discrete cosine transform in lieu of decoding the entire image at the full spatial resolution. The hybrid pipeline approach maintains ∼93% accuracy while cutting down on ∼95% of the total processing time compared to running the segmentation network at the full resolution on every image.