DAM-Net: Domain Adaptation Network With Microlabeled Fine-Tuning for Change Detection

Change detection (CD) in remote sensing imagery plays a crucial role in various applications, such as urban planning, damage assessment, and resource management. While deep learning approaches have significantly advanced CD performance, current methods suffer from poor domain adaptability, requiring extensive labeled data for retraining when applied to new scenarios. This limitation severely restricts their practical applications across different datasets. In this work, we propose DAM-Net, a domain adaptation network with microlabeled fine-tuning for CD. Our network introduces adversarial domain adaptation to CD for utilizing a specially designed segmentation-discriminator and alternating training strategy to enable effective transfer between domains. In addition, we propose a novel microlabeled fine-tuning approach that strategically selects and labels a minimal amount of samples (less than 1%) to enhance domain adaptation. The network incorporates a multitemporal transformer for feature fusion and optimized backbone structure based on previous research. Experiments conducted on the LEVIR-CD and WHU-CD datasets demonstrate that DAM-Net significantly outperforms the existing domain adaptation methods, achieving comparable performance to semisupervised approaches that require 10% labeled data while using only 0.3% labeled samples. Our approach significantly advances cross-dataset CD applications and provides a new paradigm for efficient domain adaptation in remote sensing.