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GLF-CR: SAR-enhanced cloud removal with global–local fusion [PDF]
ISPRS Journal of Photogrammetry and Remote Sensing, 2022 The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap between optical and SAR images as well as the severe speckle noise of SAR images
Xiao Xiang Zhu, Gui-Song Xia, Wen Yang
exaly +2 more sources
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
Remote Sensing, 2021 SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59%
Jun Shi, Shunjun Wei, Yue Zhou
exaly +2 more sources
Deep SAR-Net: Learning objects from signals
ISPRS Journal of Photogrammetry and Remote Sensing, 2020 This paper introduces a novel Synthetic Aperture Radar (SAR) specific deep learning framework for complex-valued SAR images. The conventional deep convolutional neural networks based methods usually take the amplitude information of single-polarization ...
Bin Lei, Mihai Datcu, Zongxu Pan
exaly +2 more sources
Research advances of SAR remote sensing for agriculture applications: A review
Journal of Integrative Agriculture, 2019 Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions.
Dongwei Yu, Zhong-Xin Chen
exaly +2 more sources
Construction of Time-series Displacement Data of Yongdam Dam Based on PSInSAR Analysis of Satellite C-band SAR Images [PDF]
Geo Data, 2023 The increase in water-related disasters due to climate change has a significant impact on the stability of water resource facilities. The displacement of a water resource facility is one of the important indicators to evaluate the stability of the ...
Taewook Kim +5 more
doaj +1 more source
Synthetic Aperture Radar (SAR) for Ocean: A Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the ...
R. M. Asiyabi +5 more
semanticscholar +1 more source
IDENTIFICATION OF INLAND-EXCESS WATER PATCHES BASED ON LiDAR AND SENTINEL 1 DATA [PDF]
Natural Resources and Sustainable Development, 2020 Understanding the habitat is essential for economic production. As a result of rapidly changing processes, features of habitats may be modified, it may be affected by a wide variety of factors.
Gálya Bernadett +6 more
doaj +1 more source
Deep Learning for SAR Ship Detection: Past, Present and Future
Remote Sensing, 2022 After the revival of deep learning in computer vision in 2012, SAR ship detection comes into the deep learning era too. The deep learning-based computer vision algorithms can work in an end-to-end pipeline, without the need of designing features manually,
Jianwei Li +4 more
semanticscholar +1 more source
A Review of Land Cover Information using H/A/Α Polarimetric Decomposition of Dual Pol Sar Data
Journal of Landscape Ecology, 2022 Information related to land use and land cover is an inevitable prerequisite for formulating any decision making for land information system. The easiest and most effective way to gather such information is via using Earth observation satellites ...
Sinha Suman
doaj +1 more source
HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation
IEEE Access, 2020 With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation.
Shunjun Wei +5 more
semanticscholar +1 more source