南京林业大学学报（自然科学版）2026，Vol.50Issue(1)：223-230,8.DOI:10.12302/j.issn.1000-2006.202407020
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
Thin cloud removal method for forestry optical remote sensing images based on slow feature analysis and generative adversarial network
摘要
Abstract
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.关键词
林业光学遥感影像/薄云去除/慢特征分析(SFA)/生成对抗网络(GANs)Key words
forestry optical remote sensing images/thin cloud removal/slow feature analysis(SFA)/generative adversarial networks(GANs)分类
农业科技引用本文复制引用
朱嵩宇,李超,景维鹏..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[J].南京林业大学学报（自然科学版）,2026,50(1):223-230,8.基金项目
黑龙江省杰出青年基金项目(JQ2023F002) （JQ2023F002）
中央高校基础研究基金项目(2572023CT16). （2572023CT16）
