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Found 5 papers, 1 papers with code
Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery
The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC).
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data.
Ranked #1 on
Image Super-Resolution
on EPFL NIR-VIS
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
To achieve this objective, a reliable and updated description of the local status of crops is required.
Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application
Real-time apple detection in orchards is one of the most effective ways of estimating apple yields, which helps in managing apple supplies more effectively.
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring.
