自然资源遥感2026，Vol.38Issue(3)：21-31,11.DOI:10.6046/zrzyyg.2025081
改进的相似像元优选方法在厚云去除算法中的应用
Application of an improved method for selecting optimal similar pixels to thick cloud removal algorithms
摘要
Abstract
Cloud cover interferes with the ground information acquisition by remote sensing satellites,while remote sensing data missing caused by thick cloud cover can be effectively addressed using developed cloud removal algorithms.However,existing algorithms,which select similar pixels by computing differences between the central pixel and its spatiotemporal neighbors,suffer from logical defects when applied to the land surface with abrupt changes.Therefore,this study proposed an improved method for selecting similar pixels while accounting for temporal variations,aiming to enhance the performance of existing cloud removal algorithms.The improved method is detailed as follows:time-varying cluster centers of time-series clean pixels are extracted using the K-means clustering method;these cluster centers are used for supervised classification of cloud-free remote sensing images,followed by the inference of the temporal variation patterns beneath clouds;since it is considered that pixels with variation patterns consistent with those of the central pixel share the identical temporal variations,the intersections of these pixels and similar pixels identified using existing algorithms are the optimal similar pixels;finally,cloud removal is performed using the original algorithm based on the determined optimal similar pixels.Using the agricultural region in north Henan as a case study,the proposed method for selecting the optimal similar pixels was applied to two cloud removal algorithms:modified neighborhood similar pixel interpolator(MNSPI)and weighted linear regression(WLR).The results indicate that the improved method can accurately extract similar pixels from rapidly changing agricultural scenarios.Both cloud removal algorithms based on the selected optimal similar pixels outperformed their original algorithms in terms of both spectral differences and structural similarity.Specifically,the improved MNSPI algorithm reduced the average root mean squared error(RMSE)from 0.023 4 to 0.015 2 and increased the structural similarity index measure(SSIM)from 0.602 3 to 0.716 6 compared to its original algorithm.Meanwhile,the improved WLR algorithm decreased the average RMSE from 0.037 5 to 0.016 8 and increased the SSIM from 0.589 7 to 0.646 1 compared to its original algorithm.Applications to real remote sensing images demonstrate that both algorithms based on the proposed method can accurately reconstruct information beneath clouds,achieving encouraging visual effects.The results of this study provide a scientific basis for removing clouds in remote sensing images of rapidly changing land surfaces,thereby offering data support for high-quality time-series remote sensing monitoring.关键词
去云算法/相似像元优选/加权线性回归/改进的相似像元插值法Key words
cloud removal algorithm/selection of optimal similar pixels/weighted linear regression(WLR)/modi-fied neighborhood similar pixel interpolator(MNSPI)分类
信息技术与安全科学引用本文复制引用
徐强,朱慧敏,陈伟强,姬广兴,李渊,郭宇龙..改进的相似像元优选方法在厚云去除算法中的应用[J].自然资源遥感,2026,38(3):21-31,11.基金项目
国家自然科学基金项目"基于生物光学空间细节信息的陆地-水色遥感影像融合研究"(编号:41701422)、"贫中营养型深水水库真光层浮游植物生物总量遥感估算研究"(编号:42071333)及国家重点研发计划项目"北方集约化农区氮素面源污染发生过程与调控机制"(编号:2021YFD1700900)共同资助. （编号:41701422）
