Research on Scene Classification Method of Remote Sensing Images with Integrated Attention Guidance
In order to address the challenge of disentangling the main subject from the background information in re-mote sensing imagery,which often complicates the extraction of effective features for scene classification tasks,a no-vel method for remote sensing image scene classification that incorporates fused attention guidance is proposed.This method seamlessly integrates a fused attention module with ShuffleNet unit modules into the lightweight network ShuffleNetV2.This integration empowers the network to effectively capture both spatial structural details and channel weighting information from remote sensing imagery,thereby synthesizing meaningful semantic insights from the ima-ges and concentrating on the core and pivotal elements of the images;this approach significantly enhances the net-work's ability to recognize features while maintaining a lightweight model.Experimental comparisons,conducted on three widely utilized public remote sensing datasets—UCM,AID and NWPU—demonstrate that the proposed method surpasses other approaches in terms of performance,thus substantiating its effectiveness.
国家科技期刊平台
NSTL
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