Active learning for interactive satellite image change detection
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 29 of its 112 references that are in the map. First posted on arXiv 8 Oct 2021.
Builds on
29 of 112 references.
References · 29
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17 more
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