Two Stage-Feature Pyramid Based Remote Sensing Images Change Detection
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Abstract
Change detection in remote sensing is a significant research focus within the field. This paper proposes a two-stage feature pyramid-based change detection network to address the challenges of pseudo-change and noise caused by semantic and spatial differences in multi-level features extracted by the encoder. The two-stage decoder was used to enhance the representation of the change feature and suppress the information interference of pseudo-change. First, the Siamese encoder network was used for bi-temporal remote sensing image encoding, feature extraction, and multi-scale initial difference feature extraction. Given the presence of excessive noise and pseudo-change information in the initial difference feature, a first-stage feature pyramid structure and a spatial-channel dual attention fusion mechanism were proposed to facilitate the interaction of semantic information and spatial information in the multi-scale difference feature, relieve the semantic difference and spatial difference of the multi-level feature, initially remove the pseudo-change information interference, and generate a multi-scale initial change feature. Subsequently, to further improve the representation of the change feature and remove the pseudo-change, the second-stage feature pyramid structure was proposed to optimize the multi-scale change feature stage by stage and then predict change detection. Finally, a series of experiments were conducted on two change detection datasets, LEVIR-CD and WHU-CD, and the experimental results proved the effectiveness of the proposed method.
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