基于频域引导特征的泥石流灾后遥感影像语义分割网络构建研究OA

[Objective]Debris flow disasters,due to their suddenness and extreme destructiveness,have become a key focus in emergency rescue.Low-altitude unmanned aerial vehicle(UAV)remote sensing can efficiently obtain high-res-olution image data of disaster-stricken areas.However,how to perform accurate and efficient semantic segmenta-tion of disaster-affected areas remains a common technical challenge in research.[Methods]This paper proposes a lightweight frequency-guided remote sensing segmentation network.[Results]The results show that:(1)Under the premise of maintaining low computational cost,the proposed method efficiently extracts the complex bound-ary structures of objects in high-resolution images,and the overall performance of the model is improved by ap-proximately 1.98%and 4.43%compared to U-Net and UKAN methods;(2)The Fast Fourier Transform(FFT)maps spatial features to the frequency domain,and by jointly modeling the real and imaginary parts,it effectively captures long-range dependencies and periodic geometric information,compensating for the limitations of tradi-tional convolution in global modeling;(3)The introduction of global semantic tokens establishes a gated fusion mechanism to adaptively regulate the weights between local features and global priors,effectively alleviating the problem of detail loss during the upsampling process.[Conclusions]This research can provide effective techni-cal support for rapid damage assessment of debris flow disasters.