Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
H Sahbi, S Deschamps - arXiv preprint arXiv:2203.11559, 2022 - arxiv.org
arXiv preprint arXiv:2203.11559, 2022•arxiv.org
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 and 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 …
on active learning. The proposed framework is iterative and relies on a question and 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 …
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 and 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. Extensive 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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