DAM-Net: Domain Adaptation Network with Micro-Labeled 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 Micro-Labeled 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. Additionally, we propose a novel Micro-Labeled Fine-Tuning approach that strategically selects and labels a minimal amount of samples (less than 1%) to enhance domain adaptation. The network incorporates a Multi-Temporal 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 existing domain adaptation methods, achieving comparable performance to semi-supervised 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. The source code of DAM-Net will be made publicly available upon publication.

SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection

Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bi-temporal images to identify changes over time. The bi-temporal spatial relationships between features at the same location at different times play a key role in this process. However, existing change detection networks often do not fully leverage these spatial relationships during bi-temporal feature extraction and fusion. In this work, we propose SRC-Net: a bi-temporal spatial relationship concerned network for CD. The proposed SRC-Net includes a Perception and Interaction Module that incorporates spatial relationships and establishes a cross-branch perception mechanism to enhance the precision and robustness of feature extraction. Additionally, a Patch-Mode joint Feature Fusion Module is introduced to address information loss in current methods. It considers different change modes and concerns about spatial relationships, resulting in more expressive fusion features. Furthermore, we construct a novel network using these two relationship concerned modules and conducted experiments on the LEVIR-CD and WHU Building datasets. The experimental results demonstrate that our network outperforms state-of-the-art (SOTA) methods while maintaining a modest parameter count. We believe our approach sets a new paradigm for change detection and will inspire further advancements in the field. The code and models are publicly available at https://github.com/Chnja/SRCNet.

RDP-Net: Region Detail Preserving Network for Change Detection

Change detection (CD) is an essential earth observation technique. It captures the dynamic information of land objects. With the rise of deep learning, neural networks (NN) have shown great potential in CD. However, current NN models introduce backbone architectures that lose the detail information during learning. Moreover, current NN models are heavy in parameters, which prevents their deployment on edge devices such as drones. In this work, we tackle this issue by proposing RDP-Net: a region detail preserving network for CD. We propose an efficient training strategy that quantifies the importance of individual samples during the warmup period of NN training. Then, we perform non-uniform sampling based on the importance score so that the NN could learn detail information from easy to hard. Next, we propose an effective edge loss that improves the network's attention on details such as boundaries and small regions. As a result, we provide a NN model that achieves the state-of-the-art empirical performance in CD with only 1.70M parameters. We hope our RDP-Net would benefit the practical CD applications on compact devices and could inspire more people to bring change detection to a new level with the efficient training strategy.