# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 1 Introduction  
The coastal zone is broadly defined as the area extending from the coastline into both land and sea, encompassing coastal plains, wetlands, estuarine deltas, tidal zones, submerged slopes, and the shallow continental shelf [1]. This region features a unique ecosystem, high biological productivity, and significant economic value [2]. Due to its favorable natural conditions and frequent human activities, the coastal zone undergoes rapid and diverse changes, primarily manifested as coastal erosion and sedimentation, sea-level rise, wetland degradation, vegetation cover loss, and land-use changes. These transformations lead to constant alterations in the geomorphological characteristics of the coastal zone: erosion causes the coastline to retreat, sedimentation pushes it forward, rising sea levels inundate low-lying areas, fluctuations in vegetation lead to frequent changes in the water environment, and human development activities drive land-use changes. Driven by these five key factors, the area of coastal wetlands decreases, biodiversity declines, and both vegetation types and density decrease, which ultimately disrupts ecological balance. Therefore, studying coastal zone changes and monitoring their trends is crucial for development planning and regulatory governance [1]. Since the concept of automated change detection was first introduced in the 1960s [3], advances in remote sensing technology have driven significant progress in change detectiontechniques [4].Remote sensing imagery allows researchers to acquire large-scale, continuous spatio-temporal data, making it particularly effective for monitoring island areas that are difficult to access directly [5]. Not only does remote sensing imagery provide direct evidence of changes in the coastal zone, but it also enables data analysis to reveal patterns and trends in these changes [6]. However, the expansion of image data increases the imbalance between change and non-change samples, leading to a category imbalance problem. This imbalance makes it difficult for change detection models to accurately identify changes in minority categories, thereby negatively affecting overall detection accuracy. Therefore, how to effectively address the class imbalance and improve the model's performance in complex scenarios has become an important challenge that current change detection techniques urgently need to solve. Unlike changes in terrestrial buildings, coastal zone changes are characterized by low color differentiation between changed and unchanged regions, along with irregular boundary shapes, making detection particularly challenging. Therefore, change detection methods must be capable of extracting and processing both global and local information [7]. Moreover, due to the frequent environmental changes in coastal zones, the demand for coastal zone change detection is primarily focused on short-term changes. The short time intervals between pre- and post-event images result in subtle changes and fewer samples, exacerbating the category imbalance problem in the dataset. Consequently, mitigating the impact of this imbalance on model accuracy is a critical issue we aim to address. Furthermore, there has been limited research on coastal zone change detection in recent years, both domestically and internationally, and the available public datasets contain very few samples of coastal zone area changes [8]. Therefore, given the unique characteristics of coastal zone changes and incorporating the recent advancements in change detection methods, designing a practical and accurate change detection approach for the coastal zone is another key challenge we seek to resolve. To address these challenges, we propose a multi-scale coastal zone change detection method (AMMNet) that incorporates multiple attention mechanisms. By modularizing different attention mechanisms, each is tasked with extracting and integrating features at various scales.