LEARNING VIRTUAL EXEMPLARS FOR LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

LEARNING VIRTUAL EXEMPLARS FOR LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION

Hichem Sahbi
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Abstract

In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question & answer model which asks the oracle (user) questions about the most informative display (subset of critical images), and according to the user's responses, updates change detections. The contribution of our framework resides in a novel display model which selects the most representative and diverse virtual exemplars that adversely challenge the learned change detection functions, thereby leading to highly discriminating functions in the subsequent iterations of active learning. Experiments, conducted on the challenging task of interactive satellite image change detection, show the superiority of the proposed virtual display model against the related work.
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Dates and versions

hal-03838832 , version 1 (03-11-2022)

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Hichem Sahbi, Sébastien Deschamps. LEARNING VIRTUAL EXEMPLARS FOR LABEL-EFFICIENT SATELLITE IMAGE CHANGE DETECTION. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2022, Kuala Lumpur, Malaysia. pp.1197-1200, ⟨10.1109/IGARSS46834.2022.9883936⟩. ⟨hal-03838832⟩
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