Results 61 to 70 of about 93,663 (165)

CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Clouds in remote sensing images inevitably affect information extraction, which hinders the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure.
Wenxuan Ge, Xubing Yang, Rui Jiang, Wei Shao, Li Zhang   +4 more
doaj   +1 more source

Water Cloud Detection with Circular Polarization Lidar: A Semianalytic Monte Carlo Simulation Approach. [PDF]

Sensors (Basel), 2022
Ahmad W, Zhang K, Tong Y, Xiao D, Wu L, Liu D.   +5 more
europepmc   +1 more source

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning. [PDF]

Sensors (Basel), 2021
Tsai CM, Lai YH, Sun YD, Chung YJ, Perng JW.   +4 more
europepmc   +1 more source

Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI

Remote Sensing
Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring.
Aleksis Pirinen, Nosheen Abid, Nuria Agues Paszkowsky, Thomas Ohlson Timoudas, Ronald Scheirer, Chiara Ceccobello, György Kovács, Anders Persson   +7 more
doaj   +1 more source

Cloud Detection and Sea Surface Temperature Retrieval by HY-1C COCTS Observations

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Sea surface temperature (SST) is a vital oceanic parameter that significantly influences air–sea heat flux and momentum exchange. SST datasets are crucial for identifying and describing both short-term and long-term climate perturbations in the ...
Ninghui Li, Lei Guan, Jonathon S. Wright
doaj   +1 more source

A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II

Remote Sensing
The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase
Caixia Yu, Xiuqing Hu, Yanyu Lu, Wenyu Wu, Dong Liu   +4 more
doaj   +1 more source

An Accurate Sea Ice Identification Method for Chinese FY-3F MERSI-III Based on Fuzzy C-Means Clustering

IEEE Access
Arctic sea ice variability is a key indicator of global climate change and is closely related to navigation safety. In this study, we propose a sea ice identification method based on fuzzy c-means (FCM) clustering, combined with a two-stage cloud ...
Han Wang, Fuqiang Zheng, Yong Hu, Cailan Gong   +3 more
doaj   +1 more source

Cloud Detection: An Assessment Study from the ESA Round Robin Exercise for PROBA-V. [PDF]

Sensors (Basel), 2020
Amato U, Antoniadis A, Carfora MF.
europepmc   +1 more source

TCSNet: A Thin-Cloud-Sensitive Network for Hyperspectral Remote Sensing Images via Spectral-Spatial Feature Fusion

Remote Sensing
Cloud detection is essential for quantitative land-surface remote sensing and cloud-climate research. However, existing methods often prioritize spatial features over spectral features, which limits thin-cloud detection. To address this issue, this paper
Yuanyuan Jia, Siwei Zhao, Xuanbin Liu, Yinnian Liu   +3 more
doaj   +1 more source

A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images.

Remote Sens Environ, 2022
Li J, Wu Z, Sheng Q, Wang B, Hu Z, Zheng S, Camps-Valls G, Molinier M.   +7 more
europepmc   +1 more source