Experimental results of different methods on the CDD datasets.

<p>SAASNets indicates our proposed change detection networks. Res2net50 indicates the initial backbone network. CPMA indicates channel position multi-head attention module. LAFEM indicates feature extraction module with special attention embedding. RASAM indicates the attention feature fusion...

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Bibliographic Details
Main Author:	Shuai Pang (4447831) (author)
Other Authors:	Chaochao You (20642598) (author), Min Zhang (111999) (author), Baojie Zhang (511505) (author), Liyou Wang (20642601) (author), Xiaolong Shi (412458) (author), Yu Sun (18463) (author)
Published:	2025
Subjects:	Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> interfered shared attention aggregation receptive field limits highly efficient channel head attention module cdd datasets show sharing channel information encode position details detecting different scales building change detection feature aggregation module scale local features irregular object buildings redundant information different stages detection performance feature extractor siamese network siamese features residual strategy position multi global features external factors experimental results encoding stage detailed semantics better accuracy
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Experimental results of different methods on the CDD datasets.

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