The Semantic Segmentation Of Remote Sensing Imagery
6 papers with code • 0 benchmarks • 1 datasets
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On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i. e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i. e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success.
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.