MSSM-SCDNet: A Multiclass Semantic Change Detection Network Suitable for Coastal Areas Based on Multiband Spatial-spectral Attention Mechanism

Abstract

Coastal change detection holds significant importance in the management of marine resources, coastal city change analysis, coastal land planning, and utilization. Deep learning-based remote sensing semantic change detection has evolved into a crucial method for identifying alterations in coastal areas. However, commonly used land cover type annotations lack specific multiclass semantic change detection type annotations unique to coastal areas. Additionally, existing datasets for coastal change detection lack rich spectral details. Therefore, this study has created a finely annotated coastal high spatial resolution multiclass semantic change detection dataset, namely CHRM-SCD, which includes 5 land cover types and 20 semantic change types. This is the first high-resolution semantic change benchmark dataset for coastal areas based on Gaofen-2 imagery. Based on this dataset, a multiclass semantic change detection network based on multiband spatial-spectral attention mechanism has been proposed in this study to achieve multiclass semantic change detection in coastal areas. It achieves 89.20% overall accuracy, 81.48% mean intersection over union, and 50.26% separated kappa coefficient, showing improvements of 7.28%, 11.58%, and 21.39% over the BiSRNet method, respectively. The stability of this research method is also demonstrated on the semantic change detection dataset. The dataset developed in this study is applicable for tasks related to detecting changes in coastal areas. The proposed method demonstrates practical effectiveness in the field of multispectral high-resolution remote sensing for coastal change detection.

Keywords

• Coastal
• deep learning
• Gaofen-2 (GF)
• high spatial resolution
• remote sensing
• semantic change detection (SCD)