基于混合注意力的遙感圖像超分辨率重建

打開文本圖片集

中圖分類號：TP391 文獻(xiàn)標(biāo)志碼：A 文章編號：1672-1098（2025）01-0064-10

Abstract：Objective To solve theproblems of local ambiguity inthe remote sensing images and lossof some detail informationinthe reconstruction.Methods A super-resolution reconstruction algorithm for remote sensing images based on dilated convolution and mixed atention was proposed.Firstly，the shallow feature map was obtained through the shallow feature extraction module，and then the convolution，dilated convolution and nonlinear activation block were combined to expandtheoverallreceptive fieldand improve thestabilityof the training proces，oasto enhance theability to express deep features.Secondly，the cascaded spatialatentionandchannel attention modules wereused to solve theproblemof high-frequency information lossFinally，the extracted features were upsampled and reconstructed to obtain high-resolution images.Results On the NWPU RESISC45and UCMerced-LandUse datasets，the simulationresults showed thatthe peak signal-to-noise ratio andthe structural similarity of the proposed algorithm were better than those of the compared algorithms，and the reconstructed images highlighted the texture details better in the subjective visual efect.Conclusion The proposed algorithm has better reconstruc tion effect and improves the quality and usability of remote sensing images.

Key Words：super-resolution reconstruction；remote sensing images；dilated convolution；attention mechanisms；Deep learning

遙感技術(shù)的不斷進(jìn)步為地球觀測提供了豐富的遙感圖像，近年來遙感圖像作為重要數(shù)據(jù)在環(huán)境監(jiān)測[1]、城市規(guī)劃、軍事偵察[2]、能源勘探等領(lǐng)域發(fā)揮著重要作用。（剩余12306字）