DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images (2003.03608v2)

Abstract: Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images. Through the dual-attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e. unchanged samples are much more than changed samples, which is one of the main reasons resulting in pseudo-changes. We put forward the weighted double margin contrastive loss to address this problem by punishing the attention to unchanged feature pairs and increase attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.1\% and 3.6\%, respectively, in the F1 score. Our Pytorch implementation is available at https://github.com/lehaifeng/DASNet.

• The paper presents a novel dual attentive Siamese network architecture called DASNet that significantly enhances change detection in satellite imagery.
• It introduces a Weighted Double-Margin Contrastive loss to address class imbalance and mitigate pseudo-change influences.
• Empirical results show a 2.9% and 4.2% F1 score improvement on CDD and BCDD datasets, respectively, affirming its robust performance.

An Overview of DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

The paper "DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images" presents a novel approach to change detection via deep learning techniques. The authors introduce DASNet, a network that utilizes the synergy of dual attention mechanisms within a Siamese architecture to enhance the detection capabilities in remote sensing applications. This method strategically addresses the challenges encountered in high-resolution satellite image change detection, focusing on mitigating pseudo-change information and managing the class imbalance inherent in change detection problems.

The proposed DASNet architecture incorporates a dual attention mechanism that captures long-range dependencies by differentiating between spatial and channel domains. This configuration allows the model to obtain discriminative feature representations, significantly enhancing its robustness and precision in identifying genuine changes as opposed to pseudo-changes. By leveraging both spatial attention and channel attention modules, DASNet improves its contextual awareness, thus avoiding the common pitfalls where noise and irrelevant variance lead to false positives in change maps.

An outstanding feature in DASNet is the implementation of a Weighted Double-Margin Contrastive (WDMC) loss function. This novel loss function is specifically engineered to address the imbalance issue by assigning differing levels of weighting for changed and unchanged feature pairs. The employment of distinct margins for contrastive evaluations ensures that the network maintains a stronger focus on more informative changed regions, while disregarding the more frequent unchanged portions. Empirical tests demonstrated DASNet's improvement over baseline methods by 2.9% and 4.2% in F1 scores for the CDD and BCDD datasets, respectively, showcasing its enhanced detection performance.

From a theoretical standpoint, the proposal of DASNet extends the understanding of metric learning and attention mechanisms in remote sensing change detection tasks. Practically, the results imply an improved efficacy in applications such as urban expansion, land cover change analysis, and disaster monitoring where precise change detection is paramount.

The paper suggests that DASNet has potential trajectories for future research. For instance, exploring its capabilities in noisy data environments, small sample settings, and open-world scenarios might unveil further strengths and adaptations of this dual attentive architecture. Such directions could solidify the model's utility in broader and more challenging operational contexts.

In conclusion, DASNet's dual attentive Siamese network design and the introduction of the WDMC loss function offer significant contributions to the field of remote sensing and change detection. These innovations represent a step forward in producing accurate and robust models capable of handling the inherent complexities of high-resolution imagery, with promising implications for future research and applications.