$\text {ResNet}_{34}$ as a backbone network to perform feature extraction. We propose the global semantic enhancement module (GSEM) to enhance the processing of high-level semantic information from a global perspective. The differential feature integration module (DFIM) is proposed to strengthen the fusion of different depth feature information, achieving learning and extraction of differential features. The entire network is trained and optimized using a deep supervision mechanism. The experimental outcomes of MFDS-Net surpass those of current mainstream change detection networks. On the LEVIR dataset, it achieved an F1 score of 91.589 and an IoU of 84.483. The code is available at https://github.com/AOZAKIiii/MFDS-Net.">

MFDS-Net: Multiscale Feature Depth-Supervised Network for Remote Sensing Change Detection With Global Semantic and Detail Information (original) (raw)

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