Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods ar...Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods are crucial for ensuring the accuracy of subsequent remote sensing analysis and applications.In view of the characteristics of hyperspectral image data,such as high dimensionality,strong spectral correlation,and high computational complexity,a threedimensional visual state space U-Net(VSSU3D)was proposed in this paper.By introducing a visual state space module into the traditional U-Net,and combining the spatial-spectral characteristics of hyperspectral images with the core idea of the Mamba model,targeted optimizations wereachieved to effectively model global information dependencies while reducing computational complexity.Additionally,a simplified channel attention module was embedded between the encoder and decoder to enhance cross-scale feature fusion capabilities.Experimental results on multiple publicly available hyperspectral image datasets demonstrated that VSSU3D achieved denoising performance comparable to or superior to existing advanced methods,which verified its effectiveness.展开更多
文摘Hyperspectral images(HSIs)are susceptible to various noise interferences during the imaging process,leading to degraded image quality and affecting the accuracy of information extraction.Efficient denoising methods are crucial for ensuring the accuracy of subsequent remote sensing analysis and applications.In view of the characteristics of hyperspectral image data,such as high dimensionality,strong spectral correlation,and high computational complexity,a threedimensional visual state space U-Net(VSSU3D)was proposed in this paper.By introducing a visual state space module into the traditional U-Net,and combining the spatial-spectral characteristics of hyperspectral images with the core idea of the Mamba model,targeted optimizations wereachieved to effectively model global information dependencies while reducing computational complexity.Additionally,a simplified channel attention module was embedded between the encoder and decoder to enhance cross-scale feature fusion capabilities.Experimental results on multiple publicly available hyperspectral image datasets demonstrated that VSSU3D achieved denoising performance comparable to or superior to existing advanced methods,which verified its effectiveness.
