A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REMOTE SENSING SCENE

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a meta-learning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.