Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference

By Xu Peng, Ruofei Zhong, Zhen Li, Qingyang Li. IEEE Transactions on Geoscience and Remote Sensing, 2021

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Remote Sensing Image Change Detection With Transformers (2021, background): “limited to the size of the RF, the attention-based approach (channel attention [9]–[12], spatial attention [9]–[11], and self-attention [2], [6], [13]) is effective in modeling global information.”
SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection (2022, methodology): “In [25,26], the authors added dense connections between the features of different layers, so as to enhance the capability of the CD network.”
RS-Mamba for Large Remote Sensing Image Dense Prediction (2024, methodology): “…the compared CNN-based models include FC-EF [36], FC-Siam-Diff [36], FC-Siam-Conc [36], STANet [39], DTCDSCN [54], SNUNet [55], CDNet [56], DDCNN [57], DASNet [58], DSIFN [59], and HANet [60], the compared transformer-based models include Change-Former [9], and the CNN-transformer hybrid models…”