Rethinking Few-Shot Remote Sensing Scene Classification: A Good Embedding Is All You Need?

Lei Xing, Yuteng Ma, Weijia Cao, Shuai Shao, Weifeng Liu, Baodi Liu

Abstract: In recent years, few-shot remote sensing scene classification (FSRSSC) has attracted more and more attention. For FSRSSC, most methods currently focus on designing a meta-learning algorithm, which obtains meta-knowledge from limited samples and then applies it to novel tasks. In this work, on one hand, we optimize the training pipeline of the feature extractor; on the other hand, we apply a novel model fusion method further to optimize the feature extractor capability of the feature extractor. We show a novel FSRSSC baseline: learning two feature representations through using two self-supervised methods on the meta-training set and then fusing the two representations into one. Then, training a linear classifier on this representation achieves state-of-the-art performance. It shows that training a good feature extractor can be more efficient than complex meta-learning algorithms for FSRSSC. We believe that our results can inspire a rethinking of FSRSSC benchmarks.