Sentinel-2 data for land cover/use mapping: A review
The advancement in satellite remote sensing technology has revolutionised the approaches
to monitoring the Earth's surface. The development of the Copernicus Programme by the …
to monitoring the Earth's surface. The development of the Copernicus Programme by the …
Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
The presence of clouds prevents optical satellite imaging systems from obtaining useful
Earth observation information and negatively affects the processing and application of …
Earth observation information and negatively affects the processing and application of …
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
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 …
optical remote sensing (RS) image data and further interferes with continuous cartography …
Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral
resolutions, and provides an opportunity for accurate and efficient inland water qauality …
resolutions, and provides an opportunity for accurate and efficient inland water qauality …
Cloud/shadow segmentation based on multi-level feature enhanced network for remote sensing imagery
S Miao, M Xia, M Qian, Y Zhang, J Liu… - International Journal of …, 2022 - Taylor & Francis
In the application of remote sensing, cloud blocking brings trouble to the analysis of surface
parameters and atmospheric parameters. Due to the complexity of the background, the …
parameters and atmospheric parameters. Due to the complexity of the background, the …
An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran
Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …
areas. This study proposed an innovative deep learning convolutional neural network (DL …
Multi-supervised feature fusion attention network for clouds and shadows detection
H Ji, M Xia, D Zhang, H Lin - ISPRS International Journal of Geo …, 2023 - mdpi.com
Cloud and cloud shadow detection are essential in remote sensing imagery applications.
Few semantic segmentation models were designed specifically for clouds and their …
Few semantic segmentation models were designed specifically for clouds and their …
Cloud detection for satellite imagery using attention-based U-Net convolutional neural network
Y Guo, X Cao, B Liu, M Gao - Symmetry, 2020 - mdpi.com
Cloud detection is an important and difficult task in the pre-processing of satellite remote
sensing data. The results of traditional cloud detection methods are often unsatisfactory in …
sensing data. The results of traditional cloud detection methods are often unsatisfactory in …
Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2
Cloud cover is a major limiting factor in exploiting time-series data acquired by optical
spaceborne remote sensing sensors. Multiple methods have been developed to address the …
spaceborne remote sensing sensors. Multiple methods have been developed to address the …