Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning
Journal:
arXiv
Published Date:
Apr 1, 2025
Abstract
Change detection has essential significance for the region's development, in
which pseudo-changes between bitemporal images induced by imaging environmental
factors are key challenges. Existing transformation-based methods regard
pseudo-changes as a kind of style shift and alleviate it by transforming
bitemporal images into the same style using generative adversarial networks
(GANs). However, their efforts are limited by two drawbacks: 1) Transformed
images suffer from distortion that reduces feature discrimination. 2) Alignment
hampers the model from learning domain-agnostic representations that degrades
performance on scenes with domain shifts from the training data. Therefore,
oriented from pseudo-changes caused by style differences, we present a
generalizable domain-agnostic difference learning network (DonaNet). For the
drawback 1), we argue for local-level statistics as style proxies to assist
against domain shifts. For the drawback 2), DonaNet learns domain-agnostic
representations by removing domain-specific style of encoded features and
highlighting the class characteristics of objects. In the removal, we propose a
domain difference removal module to reduce feature variance while preserving
discriminative properties and propose its enhanced version to provide
possibilities for eliminating more style by decorrelating the correlation
between features. In the highlighting, we propose a cross-temporal
generalization learning strategy to imitate latent domain shifts, thus enabling
the model to extract feature representations more robust to shifts actively.
Extensive experiments conducted on three public datasets demonstrate that
DonaNet outperforms existing state-of-the-art methods with a smaller model size
and is more robust to domain shift.
