One of many foremost targets of the Mars Exploration Program is to seek for proof of previous or present life on the planet. To realize this, Mars exploration has been specializing in areas which will have liquid or frozen water.
A set of vital areas might have seen cycles of ice thawing within the comparatively current previous in response to periodic adjustments within the obliquity of Mars. On this work, we use convolutional neural networks to detect floor areas containing “Mind Coral” terrain, a landform on Mars whose similarity in morphology and scale to sorted stone circles on Earth means that it could have shaped as a consequence of freeze/thaw cycles.
We use massive photos (~100-1000 megapixels) from the Mars Reconnaissance Orbiter to seek for these landforms at resolutions shut to a couple tens of centimeters per pixel (~25–50 cm). Over 52,000 photos (~28 TB) had been searched (~5% of the Martian floor) the place we discovered detections in over 200 photos. To expedite the processing we leverage a classifier community (previous to segmentation) within the Fourier area that may make the most of JPEG compression by leveraging blocks of coefficients from a discrete cosine remodel in lieu of decoding all the picture on the full spatial decision.
The hybrid pipeline method maintains ~93% accuracy whereas slicing down on ~95% of the entire processing time in comparison with operating the segmentation community on the full decision on each picture. The well timed processing of huge information units helps inform mission operations, geologic surveys to prioritize candidate touchdown websites, keep away from hazardous areas, or map the spatial extent of sure terrain. The segmentation masks and supply code can be found on Github for the group to discover and construct upon.
Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alex Morgan
Feedback: Submitted for publication, looking for feedback from the group. Code out there: this https URL
Topics: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Strategies for Astrophysics (astro-ph.IM); Machine Studying (cs.LG); Picture and Video Processing (eess.IV)
Cite as: arXiv:2311.12292 [astro-ph.EP] (or arXiv:2311.12292v1 [astro-ph.EP] for this model)
Submission historical past
From: Kyle Pearson
[v1] Tue, 21 Nov 2023 02:24:52 UTC (13,166 KB)