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• W4384787777 abstract "Heterogeneous synthetic aperture radar (SAR) data contain more information, so the use of heterogeneous SAR images can potentially improve the performance of remote sensing applications. Feature tracking is crucial for using heterogeneous SAR data. However, feature tracking is a challenge using heterogeneous SAR images to harmonize high-resolution (HR) data with coarser data. In this paper, we propose a smooth edge-guide super-resolution recurrent residual learning network to uniform resolution of heterogeneous SAR image such that their features have more consistent representation. Our proposed framework contains a super-resolution network that aims to translate the low-resolution (LR) images into the HR ones to reduce their feature differences. The generated HR final image and the HR raw image can be considered homogeneous for sea ice drift tracking. Through several examples, we demonstrate the effectiveness of the method in the feature matching of images with a large-resolution difference images from different SAR sensors." @default.
• W4384787777 created "2023-07-20" @default.
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• W4384787777 date "2023-01-01" @default.
• W4384787777 modified "2023-10-10" @default.
• W4384787777 title "Large-Resolution Difference Heterogeneous SAR Image Sea Ice Drift Tracking Using a Smooth Edge-Guide Super-Resolution Residual Network" @default.
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• W4384787777 doi "https://doi.org/10.1109/tgrs.2023.3296462" @default.
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