计算机工程与应用2025，Vol.61Issue(18)：187-197,11.DOI:10.3778/j.issn.1002-8331.2406-0114
考虑连贯语义的光学遥感图像厚云去除方法
Coherent Semantic-Driven Approach for Thick Cloud Removal in Optical Remote Sensing Images
摘要
Abstract
Thick cloud cover significantly impacts the quality of optical remote sensing images,limiting their practical applications.Deep learning methods have shown promise in addressing the challenging task of thick cloud removal.How-ever,existing approaches often suffer from issues such as blurry textures and distorted structures due to their disregard for semantic correlations and feature continuity within cloud-covered areas.To tackle these challenges,a novel coherent semantic-based two-stage generative adversarial network method for cloud removal(CSTGAN-CR)is proposed.This method effectively models the semantic correlations between cloud-covered and cloud-free regions,as well as within the cloud-covered areas,preserving contextual structures and improving the accuracy of missing part prediction.The CSTGAN-CR utilizes a two-stage deep neural network with a coherent semantic module and a multi-scale feature aggre-gation module embedded in the second stage.Experimental evaluations on the 38-cloud synthetic dataset and the RICE2 real dataset demonstrate that the proposed method generates higher-quality images compared to existing approaches,offering significant support for optical remote sensing image applications.关键词
光学遥感图像/去云/连贯语义/多尺度特征聚合Key words
optical remote sensing images/cloud removal/coherent semantics/multi-scale feature aggregation分类
信息技术与安全科学引用本文复制引用
楚玉婷,罗小波,周建军,苟永承,郭海洪..考虑连贯语义的光学遥感图像厚云去除方法[J].计算机工程与应用,2025,61(18):187-197,11.基金项目
国家科技创新合作项目(2021YFE0194700) （2021YFE0194700）
重庆市教委重点合作项目(HZ2021008). （HZ2021008）
