Benchmark Analysis of Semantic Segmentation Algorithms for Safe Planetary Landing Site Selection

This paper presents an in-depth analysis of state-of-the-art semantic segmentation algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance. Several architectures are trained from binary safety maps and the rich dataset of the High-Resolution Imaging Science Experim...

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Bibliographic Details
Published in:	IEEE access Vol. 10; pp. 41766 - 41775
Main Authors:	Claudet, Thomas, Tomita, Kento, Ho, Koki
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	aerospace safety Algorithms Artificial neural networks Benchmark testing Commercial spacecraft Complexity Computer architecture Image resolution Image segmentation Microcontrollers NASA Performance indices Planetary landing Prediction algorithms Semantic segmentation Semantics Site selection Space exploration Space flight Space vehicles
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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