Subspace prototype learning for few-Shot remote sensing scene classification

Recently, few-shot remote sensing scene classification (FSRSSC)has attracted more and more researchers' attention. The FSRSSC aims to solve the problem of how to distinguish novel categories when there are limited labeled samples quickly. The FSRSSC framework mainly includes two stages: 1) pre-training (meta-training) stage, which uses training data to train the feature extractor. 2) meta-testing stage, the trained feature extractor is used to extract the features of testing data of different categories from the training data. A limited number of labeled samples are used to train the classifier and complete the clas-sification task. In this paper, we proposed subspace prototype learning for few-shot remote sensing scene classification method (SPL). To improve the generalization performance of the feature extractor in the pre-training stage and the robustness of the classifier in the meta-testing stage, we improve the existing methods from two aspects. On the one hand, we introduce the pre-trained model on the natural images. Then we use remote sensing data to fine-tune the pre-trained model to solve the problem of negative transfer caused by the differences between natural and remote sensing images. On the other hand, in the meta-test phase, we use the subspace learning method to learn a subspace prototype for each type of sample and complete the classification task by measuring the distance between the samples and the prototype, which achieves performance classification performance.(c) 2023 Elsevier B.V. All rights reserved.