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- W4382053154 abstract "Change detection from comparing SAR and optical images is a challenging problem. The popular solution is to map the two images into a common domain, and then do comparing. In general, the mapping algorithm used convolution as the backbone. However, the convolution operation has difficulties in extracting global information in space-time. This paper proposes to replace convolution by transformer. In specific, we first use a stacked convolution denoise autoencoder (SCDAE) to extract deep semantic features of each image. Then we design a transformer-based cross-mapping autoencoder (TCMA) to transform the features into a common representation space. Finally, we compare the two common-domain feature maps to obtain the difference map, and refine the difference map by similarity measurement and clustering algorithm. The parameters of the two steps above are learned by commonalities between feature maps instead of labels, i.e. unsupervised learning. The proposed algorithm is named as cross-mapping network method (CM-Net)." @default.
- W4382053154 created "2023-06-27" @default.
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- W4382053154 date "2023-04-21" @default.
- W4382053154 modified "2023-10-12" @default.
- W4382053154 title "Cross-Mapping Net: Unsupervised Change Detection from Heterogeneous Remote Sensing Images Using a Transformer Network" @default.
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- W4382053154 doi "https://doi.org/10.1109/icccs57501.2023.10150445" @default.
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