Local-global Semantic Feature Enhancement Model for Remote Sensing Imagery Change Detection

Convolutional Neural Network (CNN) has achieved promising results in change detection using remote sensing images. However, CNN performs poorly on global semantic information extraction due to its limited receptive field. To this end, we propose an end- to- end encoding- decoding local- global feature enhancement network, termed as LGE-Net, which introduces locally enhanced Transformers (LE-Transformer) for capturing global semantic feature representation. Specifically, the LGE-Net uses the CNN backbone network to obtain local semantic features of dual-phase remote sensing images and cascades the extracted local features into the LE-Transformer layer to extract deep global semantic features. Then, in the decoder, the features are cascaded, up-sampled, and finally connected with multi-scale local features by semantic enhancement modules (CEMs). In addition, a local feature- enhanced feed- forward network (LEFFN) is designed to enhance local information interaction in the LE-Transformer blocks and their adjacent blocks. Extensive experiments on the two publicly available datasets (i.e., LEVIR-CD and CDD) show that the proposed LGE-Net can accurately and efficiently identify changed regions, reduce false and missed detections, and thus has a better generalization ability, compared to other state-of-the-art change detection methods. © 2023 Research Institute of Beijing. All rights reserved.