Jun 1, 2017 · Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to …

Apr 29, 2021 · In this review, the different conventional CD methods based on threshold, time differentiation, machine learning, and the intelligent algorithms including convolution neural …

We collect the latest open-source tools and datasets for cloud and cloud shadow detection, and launch this online project (Open Satellite Image Cloud Detection Resources, i.e., OpenSICDR) …

Jun 1, 2021 · With so many different cloud detection algorithms available in the literature, it is extremely important to compare the performances of all those approaches and provide …

Machine (SVM) classifier to identify clouds from non-cloud regions using local cloud patterns. With the recent advances made in deep-learning algorithms for image segme. tation, several …

Cloud detection in satellite images is an important first-step in many remote sensing applications. Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing …

In this study, a dynamic threshold cloud detection algorithm is proposed based on the FY-3E\MERSI-LL infrared channel, combined with auxiliary data such as sea surface temperature …

We examined the relative performance of five different cloud-masking algorithms (Sen2Cor, MAJA, LaSRC, Fmask and Tmask) in 6 Sentinel-2 scenes (28 total images) distributed across …

Dec 14, 2019 · Cloud detection techniques are employed by researchers using physical parameters of the cloud, such as (a) shape attributes; (b) fusion of multi-scale convolutional …

Aug 1, 2019 · In this paper, we introduce the Remote Sensing Network (RS-Net), a deep learning model for detection of clouds in optical satellite imagery, based on the U-net architecture.