Super-resolution Reconstruction for Remote Sensing Images of Open-pit Coal Mines Based on CNN and Swin Transformer
Aiming at the problems of weak feature extraction ability and insufficient feature utilization of existing super-resolution reconstruction models of remote sensing images of open-pit mines,a new super-resolution reconstruction method of open-pit remote sensing images based on deep convolutional neural network and Swin Transformer is proposed.Firstly,convolu-tional neural network and Swin Transformer network are used to map the remote sensing images of open-pit mine to the global and local feature spaces,and fully extract the deep features of the remote sensing images.Then,a multi-scale feature fusion net-work based on attention mechanism is constructed to realize the deep fusion of local and global features of remote sensing ima-ges and strengthen the distinguishing ability of effective feature expression.Finally,the deep fusion features are used as the in-put of the super-resolution decoding module to reconstruct high-resolution remote sensing images of open-pit mines.Through testing on the self-built open-pit mine image dataset and open source data set,the experimental results show that compared with the current mainstream image super-resolution reconstruction algorithms,the proposed method has better visual perception and lower RMSE than other comparison methods.
NETL
NSTL
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