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14 June 2023 Efficient segmentation of Martian dust storms
Ethan R. Adams, Jared C. Riley, Arthur C. Depoian II, Colleen P. Bailey, Parthasarathy Guturu
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Proceedings Volume 12549, Unmanned Systems Technology XXV; 1254909 (2023) https://doi.org/10.1117/12.2664104
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
As one of the classic fields within the area of computer vision, image classification and segmentation solutions as topics have expanded exponentially in terms of accuracy and ease of use. On Mars, the atmospheric and surface conditions can lead to the sudden onset of a dust storm, or a more common dust devil, causing a multitude of issues for both equipment and crew. The ability to identify and locate area which should be avoided due to these storms is necessary for mission safety. Many current techniques are not practical due to being hefty and computationally expensive for specific tasks that require the ability for swift deployability onto systems with more stringent constraints. This paper proposes a novel approach to the problem of segmentation by marrying an efficient yet powerful Vision Transformer based model with traditional signal processing techniques to ensure peak performance. With the National Aeronautics and Space Administration (NASA) looking to land a team on Mars, this paper takes on the real time hurdle of classifying and segmenting dust storms within remote satellite equatorial photos, using a model designed to be integrated on any and all future systems, increasing overall mission success.
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Ethan R. Adams, Jared C. Riley, Arthur C. Depoian II, Colleen P. Bailey, and Parthasarathy Guturu "Efficient segmentation of Martian dust storms", Proc. SPIE 12549, Unmanned Systems Technology XXV, 1254909 (14 June 2023); https://doi.org/10.1117/12.2664104
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KEYWORDS
Mars
Image segmentation
Object detection
Planets
Safety
Signal processing
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