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Found 5 papers, 3 papers with code
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.
Fast Classification with Sequential Feature Selection in Test Phase
During the test phase, the proposed approach utilizes Fisher scores for feature ranking to identify the most important feature at each step.
Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran
For this purpose, AOD products were fused by machine learning algorithms using different fusion strategies at two levels: the data level and the decision level.
Deep Feature Selection using a Teacher-Student Network
In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension.
Deep Learning-Based Channel Estimation
This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel.
