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Found 5 papers, 3 papers with code
QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering
The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night.
On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios.
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification
Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results.
A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks
In recent years, machine learning (ML) algorithms have become widespread in all the fields of remote sensing (RS) and earth observation (EO).
