Segmentation Of Remote Sensing Imagery
12 papers with code • 0 benchmarks • 3 datasets
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Most implemented papers
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
EarthMapper: A Tool Box for the Semantic Segmentation of 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 MONITORING WITH WEBCAMS
Continuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system.
Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods.
Lake Ice Detection from Sentinel-1 SAR with Deep Learning
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.
Photi-LakeIce Dataset
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
A Method for Detection of Small Moving Objects in UAV Videos
To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds.
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images.