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arXiv AI By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc

Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations

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The paper introduces MaSoN, an end-to-end unsupervised remote sensing change detection framework that synthesises diverse changes directly in latent feature space during training. By generating changes based on feature statistics of the target data, MaSoN produces data‑driven variations that align with the target domain and can be applied to new modalities such as SAR and multispectral imagery. The method achieves a 14.1 percentage point improvement in average F1 score across five benchmarks, demonstrating strong generalisation across diverse change types.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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