Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification

In recent years, few-shot remote sensing scene classification has attracted significant attention, aiming to obtain excellent performance under the condition of insufficient sample numbers. A few-shot remote sensing scene classification framework contains two phases: (i) the pre-training phase seeks to adopt base data to train a feature extractor, and (ii) the meta-testing phase uses the pre-training feature extractor to extract novel data features and design classifiers to complete classification tasks. Because of the difference in the data category, the pre-training feature extractor cannot adapt to the novel data category, named negative transfer problem. We propose a novel method for few-shot remote sensing scene classification based on shared class Sparse Principal Component Analysis (SparsePCA) to solve this problem. First, we propose, using self-supervised learning, to assist-train a feature extractor. We construct a self-supervised assisted classification task to improve the robustness of the feature extractor in the case of fewer training samples and make it more suitable for the downstream classification task. Then, we propose a novel classifier for the few-shot remote sensing scene classification named Class-Shared SparsePCA classifier (CSSPCA). The CSSPCA projects novel data features into subspace to make reconstructed features more discriminative and complete the classification task. We have conducted many experiments on remote sensing datasets, and the results show that the proposed method dramatically improves classification accuracy.