Multi-feature embedded learning SVM for cloud detection in remote sensing images. (September 2022)

• Record Type: Journal Article
• Authors: Zhang, Weidong; Jin, Songlin; Zhou, Ling; Xie, Xiwang; Wang, Fangyuan; Jiang, Lili; Zheng, Ying; Qu, Peixin; Li, Guohou; Pan, Xipeng
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Cloud detection -- Remote sensing images -- Multi-feature space -- Support vector machine; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108177
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: To improve remote sensing image transmission efficiency, we propose a cloud detection method using a multi-feature embedded learning support vector machine (SVM) to address cloud coverage occupying channel transmission bandwidth. Specifically, we first consider the imaging and physical properties of the clouds to construct a multi-feature space of cloud and non-cloud samples, which mainly includes five valuable features of grayscale, geometry, contrast, correlation, and angular second moment. Subsequently, we regard cloud detection (CDRSI) of remote sensing images as a binary classification problem, and construct a classifier by using multi-feature embedded learning SVM. Finally, the CDRSI is implemented by image block operations. Additionally, we build a large-scale real-world Remote Sensing Image Cloud Detection Benchmark (RSICDB) including 1520 images, where 790 non-cloud images and 430 cloud images are used as training datasets, 150 of which as test samples with the corresponding 150 mask results. Experimental results demonstrate that the proposed method can detect clouds with higher accuracy and robustness than compared methods.