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TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification
2021
Remote Sensing
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this
doi:10.3390/rs14010111
fatcat:6qjuedqtabhzro67e7tsikbweq