In conventional remote sensing change detection (RS CD) procedures, extensive
manual labeling for bi-temporal images is first required to maintain the
performance of subsequent fully supervised training. However, pixel-level
labeling for CD tasks is very complex and time-consuming. In this paper, we
explore a novel self-supervised contrastive framework applicable to the RS CD
task, which promotes the model to accurately capture spatial, structural, and
semantic information through domain adapter and hierarchical contrastive head.
The proposed SSLChange framework accomplishes self-learning only by taking a
single-temporal sample and can be flexibly transferred to main-stream CD
baselines. With self-supervised contrastive learning, feature representation
pre-training can be performed directly based on the original data even without
labeling. After a certain amount of labels are subsequently obtained, the
pre-trained features will be aligned with the labels for fully supervised
fine-tuning. Without introducing any additional data or labels, the performance
of downstream baselines will experience a significant enhancement. Experimental
results on 2 entire datasets and 6 diluted datasets show that our proposed
SSLChange improves the performance and stability of CD baseline in data-limited
situations. The code of SSLChange will be released at
https://github.com/MarsZhaoYT/SSLChange

该论文介绍了一种新颖的自监督对比框架，适用于遥感变化检测任务，通过领域适配器和分层对比头提高模型对空间、结构和语义信息的准确捕捉，通过自学习使用单个时态样本，可灵活转移到主流变化检测基线，提供无需额外数据或标签的下游基线性能显著增强，实验结果表明所提出的 SSLChange 改善了数据有限情况下变化检测基线的性能和稳定性。

基于域适应的自监督变化检测框架 SSLChange

SSLChange: A Self-supervised Change Detection Framework Based on Domain  Adaptation

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we
investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific
case of UDA where a model is adapted to a target domain without access to
source data. We propose a novel approach for the SF-UDA setting based on a loss
reweighting strategy that brings robustness against the noise that inevitably
affects the pseudo-labels. The classification loss is reweighted based on the
reliability of the pseudo-labels that is measured by estimating their
uncertainty. Guided by such reweighting strategy, the pseudo-labels are
progressively refined by aggregating knowledge from neighbouring samples.
Furthermore, a self-supervised contrastive framework is leveraged as a target
space regulariser to enhance such knowledge aggregation. A novel negative pairs
exclusion strategy is proposed to identify and exclude negative pairs made of
samples sharing the same class, even in presence of some noise in the
pseudo-labels. Our method outperforms previous methods on three major
benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C
and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS
with +12.3% in the single-source setting and +6.6% in multi-target adaptation.
Additional analyses demonstrate that the proposed approach is robust to the
noise, which results in significantly more accurate pseudo-labels compared to
state-of-the-art approaches.

本文提出一种基于损失加权策略的 Source-free Unsupervised Domain Adaptation 方法，该方法在没有源数据的情况下，通过估计伪标签的不确定性来逐步优化伪标签，并采用一种自监督对比框架作为目标空间规范化器来增强知识聚合，并在三个基准测试中均取得了显著的优异表现，证明了该方法的鲁棒性。