# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 2 Related Work  
### Deep Learning-Based Change Detection Method  
In recent years, deep learning methods, with their powerful data processing capabilities and automatic feature extraction advantages, have achieved comparable results with simpler architectures [9]. However, due to the complexity of deep learning models and their reliance on large datasets, they are more significantly affected by the category imbalance problem. To address this, researchers have proposed various deep learning network architectures that better handle category imbalance, many of which are based on twin network structures. Twin networks use a pair of deep neural networks with shared weights to independently extract the key features from pre- and post-event remote sensing images. By analyzing the differences between these features, the networks identify areas of change and generate a map that highlights the detected changes [10]. For example, the DTCDSCN model proposed by [PERSON] et al. [11] introduces a deep convolutional network with dual-task constraints, enabling simultaneous change detection and semantic segmentation. By using a shared feature extraction layer, it improves the discriminative power of the extracted features and resolves the issue of insufficient feature discrimination in traditional methods. Meanwhile, the MSCANet model proposed by [PERSON] et al. [12] combines the strengths of CNNs and transformers, capturing long-range dependencies via a multi-scale context aggregation module, which is particularly effective for detecting fine-grained changes in high-resolution remote sensing images. [PERSON] et al.'s [13] AMTNet introduces a multi-scale transformer-based attention mechanism to model spatio-temporal contextual information effectively. Additionally, it uses a feature exchange module to partially exchange features between different time domains, reducing inter-domain differences and improving change detection accuracy and robustness. The primary advantage of twin networks lies in their ability to effectively capture the similarities and differences between two images through shared weights, thereby improving detection accuracy. The shared and co-optimized parameters between the two branches also help reduce the risk of overfitting.  
We hope that in the complex and changing coastal zone environment, different levels of useful features can be effectively combined to better capture the variability between pre- and post-images to improve the model's ability to perceive complex changes. However, single-stream methods can suffer from feature confusion, information redundancy, and loss of spatial information due to the need to cascade together the pre- and post-image inputs, which affects the detection accuracy of the model. Therefore, we designed a multi-scale twin network using the idea of dual-stream methods. Multi-scale processing can effectively extract feature information at different levels to avoid the problem of information loss that may occur at a single scale, and the structure of the twin network allows the pre- and post-images to be processed separately through independent network branches, thus avoiding the problems of feature confusion and information redundancy. This design can retain the useful spatial information in the image more precisely, enhance the model's perceptual ability in the face of complex changes, and ultimately improve the accuracy of detection.