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DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images
2021
Remote Sensing
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial
doi:10.3390/rs13183755
fatcat:jjizsdld5vc7npyc2va6brd2re