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Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data.
Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals
Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps.
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