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Image augmentation with invertible networks in interactive satellite image change detection

arXiv 2510.18660 ↗First posted 21 Oct 2025cs.CVcited by 0

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Abstract (arXiv)

This paper devises a novel interactive satellite image change detection algorithm based on active learning. Our framework employs an iterative process that leverages a question-and-answer model. This model queries the oracle (user) about the labels of a small subset of images (dubbed as display), and based on the oracle's responses, change detection model is dynamically updated. The main contribution of our framework resides in a novel invertible network that allows augmenting displays, by mapping them from highly nonlinear input spaces to latent ones, where augmentation transformations become linear and more tractable. The resulting augmented data are afterwards mapped back to the input space, and used to retrain more effective change detection criteria in the subsequent iterations of active learning. Experimental results demonstrate superior performance of our proposed method compared to the related work.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 51 of its 153 references that are in the map. First posted on arXiv 21 Oct 2025.

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51 of 153 references.

References · 51

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38 more
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