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[HTML][HTML] Sea ice extraction via remote sensing imagery: algorithms, datasets, applications and challenges
W Huang, A Yu, Q Xu, Q Sun, W Guo, S Ji, B Wen… - Remote Sensing, 2024 - mdpi.com
Deep learning, which is a dominating technique in artificial intelligence, has completely
changed image understanding over the past decade. As a consequence, the sea ice …
changed image understanding over the past decade. As a consequence, the sea ice …
Sea ice extraction via remote sensed imagery: Algorithms, datasets, applications and challenges
A Yu, W Huang, Q Xu, Q Sun, W Guo, S Ji… - ar** from MODIS TOA reflectance data
The topic of satellite remote sensing of lake ice has gained considerable attention in recent
years. Optical satellite data from the Moderate Resolution Imaging Spectroradiometer …
years. Optical satellite data from the Moderate Resolution Imaging Spectroradiometer …
[HTML][HTML] Lake ice-water classification of RADARSAT-2 images by integrating IRGS Segmentation with pixel-based random forest labeling
Changes to ice cover on lakes throughout the northern landscape has been established as
an indicator of climate change and variability, expected to have implications for both human …
an indicator of climate change and variability, expected to have implications for both human …
Estimating sea ice concentration from SAR: Training convolutional neural networks with passive microwave data
Historically, sea ice concentration (SIC) has been measured through the use of passive
microwave sensors, as well as human interpretation of synthetic aperture radar (SAR) …
microwave sensors, as well as human interpretation of synthetic aperture radar (SAR) …
Fractal analysis and texture classification of high-frequency multiplicative noise in SAR sea-ice images based on a transform-domain image decomposition method
IH Shahrezaei, HC Kim - IEEE Access, 2020 - ieeexplore.ieee.org
Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of
scene backscattering and the texture due to noncoherent high-frequency multiplicative noise …
scene backscattering and the texture due to noncoherent high-frequency multiplicative noise …
[HTML][HTML] The use of C-band and X-band SAR with machine learning for detecting small-scale mining
G Janse van Rensburg, J Kemp - Remote Sensing, 2022 - mdpi.com
Illicit small-scale mining occurs in many tropical regions and is both environmentally and
socially hazardous. The aim of this study was to determine whether the classification of …
socially hazardous. The aim of this study was to determine whether the classification of …
[HTML][HTML] River Ice Map** from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the …
H Han, T Kim, S Kim - Remote Sensing, 2024 - mdpi.com
Accurate river ice map** is crucial for predicting and managing floods caused by ice jams
and for the safe operation of hydropower and water resource facilities. Although satellite …
and for the safe operation of hydropower and water resource facilities. Although satellite …
Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data
H Han, H Kim - Remote Sensing of Environment, 2018 - Elsevier
Satellite passive microwave (PM) sensors have observed sea ice in Polar Regions and
provided sea ice concentration (SIC) data since the 1970s. SIC has been used as a primary …
provided sea ice concentration (SIC) data since the 1970s. SIC has been used as a primary …
[HTML][HTML] Retrieval of summer sea ice concentration in the Pacific Arctic Ocean from AMSR2 observations and numerical weather data using random forest regression
H Han, S Lee, HC Kim, M Kim - Remote Sensing, 2021 - mdpi.com
The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change
and important information for the development of a more economically valuable Northern …
and important information for the development of a more economically valuable Northern …