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SDGSAT-1 Cloud Detection Algorithm Based on RDE-SegNeXt

作     者:Li, Xueyan Hu, Changmiao 

作者机构:Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China 

出 版 物:《REMOTE SENSING》 (Remote Sens.)

年 卷 期:2025年第17卷第3期

页      面:470-470页

核心收录:

学科分类:0830[工学-环境科学与工程(可授工学、理学、农学学位)] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基  金:Civil Aerospace Technology Pre-research Project of China's 14th Five-Year Plan [D040404] Youth Innovation Promotion Association, CAS The "Future Star" Talent Plan of Aerospace Information Research Institute, Chinese Academy of Sciences [2021KTYWLZX07] 

主  题:SDGSAT-1 cloud detection re-parameterized convolution detail-enhanced convolution convolutional attention 

摘      要:This paper proposes an efficient cloud detection algorithm for Sustainable Development Scientific Satellite (SDGSAT-1) data. The core work includes the following: (1) constructing a SDGSAT-1 cloud detection dataset containing five types of elements: clouds, cloud shadow, snow, water body, and land, with a total of 15,000 samples;(2) designing a multi-scale convolutional attention unit (RDE-MSCA) based on a gated linear unit (GLU), with parallel re-parameterized convolution (RepConv) and detail-enhanced convolution (DEConv). This design focuses on improving the feature representation and edge detail capture capabilities of targets such as clouds, cloud shadow, and snow. Specifically, the RepConv branch focuses on learning a new global representation, reconstructing the original multi-branch deep convolution into a single-branch structure that can efficiently fuse channel features, reducing computational and memory overhead. The DEConv branch, on the other hand, uses differential convolution to enhance the extraction of high-frequency information, and is equivalent to a normal convolution in the form of re-parameterization during the inference stage without additional overhead;GLU then realizes adaptive channel-level information regulation during the multi-branch fusion process, which further enhances the model s discriminative power for easily confused objects. It is integrated into the SegNeXt architecture based on RDE-MSCA and proposed as RDE-SegNeXt. Experiments show that this model can achieve 71.85% mIoU on the SDGSAT-1 dataset with only about 1/12 the computational complexity of the Swin-L model (a 2.71% improvement over Swin-L and a 5.26% improvement over the benchmark SegNeXt-T). It also significantly improves the detection of clouds, cloud shadow, and snow. It achieved competitive results on both the 38-Cloud and LoveDA public datasets, verifying its effectiveness and versatility.

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