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Frugal Learning for Interactive Satellite Image Change Detection

Abstract : We introduce in this paper a novel active learning algorithm for satellite image change detection. The proposed solution is interactive and based on a question and answer model, which asks an oracle (annotator) the most informative questions about the relevance of sampled satellite image pairs, and according to the oracle's responses, updates a decision function iteratively. We investigate a novel framework which models the probability that samples are relevant; this probability is obtained by minimizing an objective function capturing representativity, diversity and ambiguity. Only data with a high probability according to these criteria are selected and displayed to the oracle for further annotation. Extensive experiments on the task of satellite image change detection after natural hazards (namely tornadoes) show the relevance of the proposed method against the related work.
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Submitted on : Tuesday, November 16, 2021 - 4:05:39 PM
Last modification on : Wednesday, January 19, 2022 - 2:08:02 PM
Long-term archiving on: : Thursday, February 17, 2022 - 8:08:52 PM

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Hichem Sahbi, Sébastien Deschamps, Andrei Stoian. Frugal Learning for Interactive Satellite Image Change Detection. IEEE International Geoscience and Remote Sensing Symposium, Jul 2021, Brussels, Belgium. pp.2811-2814, ⟨10.1109/IGARSS47720.2021.9553385⟩. ⟨hal-03431343⟩

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