Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
Sign Up to like & getrecommendations! Published in 2020 at "Remote Sensing of Environment"
DOI: 10.1016/j.rse.2020.112045
Abstract: Abstract Cloud cover is a common and inevitable phenomenon that often hinders the usability of optical remote sensing (RS) image data and further interferes with continuous cartography based on RS image interpretation. In the literature,… read more here.
Keywords: remote sensing; image; cloud detection; deep networks ... See more keywords
Cloud detection using infrared atmospheric sounding interferometer observations by logistic regression
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Remote Sensing"
DOI: 10.1080/2150704x.2018.1553318
Abstract: ABSTRACT Hyper-spectral infrared radiance data play an important role in cloud detection. To improve the cloud detection accuracy, this study proposes a novel cloud detection method based on the logistic regression model that uses the… read more here.
Keywords: logistic regression; cloud detection; infrared atmospheric; sounding interferometer ... See more keywords
LCDNet: Light-Weighted Cloud Detection Network for High-Resolution Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3181303
Abstract: Cloud detection is one of the important links in high-resolution remote sensing image processing. Cloud detection methods can be mainly divided into the following three categories: threshold methods, clustering methods based on machine learning, and… read more here.
Keywords: network; remote sensing; detection; light weighted ... See more keywords
A Novel Spectral Indices-Driven Spectral-Spatial-Context Attention Network for Automatic Cloud Detection
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2023.3260203
Abstract: Cloud detection is a fundamental step for optical satellite image applications. Existing deep learning methods can provide more accurate cloud detection results. However, the performance of these methods relies on a large number of label… read more here.
Keywords: cloud detection; detection; spectral spatial; spatial context ... See more keywords
Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3023683
Abstract: Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared sounders is essential for numerical weather prediction. A new cloud detection model is developed for the cross-track infrared sounder (CrIS) using the artificial… read more here.
Keywords: cris; accuracy; detection; model ... See more keywords
Scene Aggregation Network for Cloud Detection on Remote Sensing Imagery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3029778
Abstract: There has been a breakthrough in cloud detection by using convolutional neural networks (CNNs) during these years. However, there are still weaknesses among current cloud detection algorithms because only cloud mask information is used. As… read more here.
Keywords: network; scene information; scene aggregation; detection ... See more keywords
DCNet: A Deformable Convolutional Cloud Detection Network for Remote Sensing Imagery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3086584
Abstract: Recently, deep convolutional neural networks (CNNs) have made important progress in cloud detection with powerful representation learning capability and yield significant performance. However, most existing CNN-based cloud detection methods still face serious challenges because of… read more here.
Keywords: convolutional cloud; deformable convolutional; detection; detection network ... See more keywords
Ground-Based Cloud Detection Using Multiscale Attention Convolutional Neural Network
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3106337
Abstract: Cloud detection plays a significant role in ground-based remote sensing observation, and it is quite challenging due to the variations in illumination and cloud form, and the vague boundaries between cloud and sky. In this… read more here.
Keywords: ground based; multiscale attention; detection; cloud detection ... See more keywords
Cloud Detection From High-Resolution Remote Sensing Images Based on Convolutional Neural Networks With Geographic Features and Contextual Information
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2023.3251364
Abstract: The diversity and complexity of the subsurface, as well as the similarity of cloud features to those of highlighted surface objects (especially snowy areas), are the main reasons for the current limitations in cloud detection… read more here.
Keywords: geographic features; contextual information; snow covered; detection ... See more keywords
Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2917612
Abstract: The whole-sky imager has been increasingly used for ground-based cloud automatic observation. Many approaches based on image processing have been applied to detect or classify clouds in whole-sky images (WSIs). However, most of the studies… read more here.
Keywords: whole sky; cloud detection; fine grained; recognition ... See more keywords
Unsupervised Domain Adaptation for Cloud Detection Based on Grouped Features Alignment and Entropy Minimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3067513
Abstract: Most convolutional neural network (CNN)-based cloud detection methods are built upon the supervised learning framework that requires a large number of pixel-level labels. However, it is expensive and time-consuming to manually annotate pixelwise labels for… read more here.
Keywords: domain adaptation; grouped features; domain; cloud detection ... See more keywords