Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
Source abstract
Abstract
Accurate coastal zone change detection is crucial for coastal urban planning and marine resource development. To address the specificity of coastal zone change detection and the category imbalance issue in the model, we propose a multi-scale coastal zone change detection method (AMMNet) based on the attention mechanism. The method leverages multi-scale features extracted by the ResNet backbone, which are then optimized and integrated through high-frequency attention and spatio-temporal difference modules. These modules allow the model to focus on both global and local changes, enhancing its ability to detect variations in coastal zones. Additionally, the foreground attention module refines the model’s attention on relevant regions, ensuring improved performance. The experimental results show that our method achieves the highest scores in several evaluation metrics, demonstrating significant advantages in accuracy and generalization and effectively addressing the category imbalance problem. It provides a robust solution for coastal zone change detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
National Marine Information Center, Tianjin 300171, China
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
Funding
Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources
References (22)
Show source-provided references
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