Deep hierarchical transformer for change detection in high-resolution remote sensing images

Deep hierarchical transformer for change detection in high-resolution remote sensing images

Blog Article

ABSTRACTDeep learning instantiated by convolutional neural networks has achieved great success in high-resolution remote-sensing sten jacket m image change detection.However, such networks have a limited receptive field, being unable to extract long-range dependencies in a scene.As the transformer model with self-attention can better describe long-range dependencies, we introduce a hierarchical transformer model to improve the precision of change detection in high-resolution remote sensing images.First, the hierarchical transformer extracts abstract features from multitemporal remote sensing images.

To effectively minimize the model’s complexity and enhance the feature representation, we limit the self-attention calculation of each transformer layer to local windows with different sizes.Then, we combine the features extracted by the hierarchical transformer and input them into a nested U-Net to obtain the change detection results.Furthermore, a simple but effective model fusion strategy is adopted to improve the change detection accuracy.Extensive experiments are carried out on yale law school colors two large-scale data sets for change detection, LEVIR-CD and SYSU-CD.

The quantitative and qualitative experimental results suggest that the proposed method outperforms the advanced methods in terms of detection performance.