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Semi-MCNN: A Semi-supervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Sub-meter HRRS Images
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Sub-meter high-resolution remote sensing (HRRS) image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning (DL) based models have achieved remarkable performance in many land cover classification tasks through end-to-end supervised learning. However, the excellent performance of DLbased models relies heavily on a large number of well-annotated samples, which is impossible in practical land cover classification scenarios.
doi:10.1109/jstars.2020.3019410
fatcat:jtzkx4uhojh5jh6nmnrmgim3ce