基于慢特征分析与生成对抗网络的林业光学遥感影像薄云去除方法OA

To address the issue of image distortion and reduced usability caused by thin cloud removal in optical remote sensing images,this study proposes a novel thin cloud removal method:SFGAN,that integrates slow feature analysis(SFA)with generative adversarial networks(GANs),aiming to enhance image quality and provide reliable data support for forestry remote sensing analysis.[Method]First,a slow-varying feature module is designed to calculate cloud reflectance and high-dimensional feature slowness.The slow-varying feature vectors are concatenated with random initial vectors as the generator input,improving cloud feature recognition.Second,cloud reflectance is utilized as a discriminative constraint factor to iteratively optimize the discriminator,thereby generating high-quality cloud-free images through adversarial training.[Result]Experiments on public datasets RICE1 and PRSC demonstrate that the SFGAN outperforms existing methods in both quantitative metrics(e.g.,PSNR=33.740 7 and SSIM=0.958 2 on RICE1,PSNR=24.341 3 and SSIM=0.879 2 on PRSC)and visual assessments.Validation using Landsat 8 imagery shows SFGAN achieves superior cloud removal effects in both real and simulated cloud scenarios,with a processing time of 0.98 seconds per image.[Conclusion]The SFGAN framework effectively mitigates thin cloud interference in forestry optical remote sensing images by synergizing SFA and GANs,significantly improving data usability and analytical accuracy at the source level.