Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images

Abstract

Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary Change Detection (BCD) since it enables detailed change analysis in the observed areas. Previous works established triple-branch Convolutional Neural Network (CNN) architectures as the paradigm for SCD. However, it remains challenging to exploit semantic information with a limited amount of change samples. In this work, we investigate to jointly consider the spatio-temporal dependencies to improve the accuracy of SCD. First, we propose a Semantic Change Transformer (SCanFormer) to explicitly model the 'from-to' semantic transitions between the bi-temporal RSIs. Then, we introduce a semantic learning scheme to leverage the spatio-temporal constraints, which are coherent to the SCD task, to guide the learning of semantic changes. The resulting network (SCanNet) significantly outperforms the baseline method in terms of both detection of critical semantic changes and semantic consistency in the obtained bi-temporal results. It achieves the SOTA accuracy on two benchmark datasets for the SCD.

Cited by in corpus (7)

• Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
• Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
• A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
• ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer
• Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
• Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework
• Enhanced SCanNet with CBAM and Dice Loss for Semantic Change Detection