META TRANSFER LEARNING FOR FEW-SHOT HYPERSPECTRAL IMAGE CLASSIFICATION

We propose a novel meta-learning approach for few-shot hyperspectral image (HSI) classification, which learns to distil transferable prior knowledge from a base dataset with sufficient labeled samples and generalize the knowledge to an unseen dataset with extremely limited labeled samples for performance improvement. Specifically, we first construct a backbone classification model using an embedding module and a linear classifier. Then, we sample extensive synthetic few-shot tasks from the base dataset, each of which consists of a support set with limited labeled samples and a query set with some unlabeled test samples. Given these tasks, we propose to optimize the embedding module using an episode learning scheme where for each task we train the linear classier based on an initialized embedding module using the support set and ultimately optimize the embedding module based on the test error on the query set until the test error on all tasks is minimized. By doing this, the resultant embedding module is able to appropriately generalize to an unseen few-shot classification task and lead to good performance with the linear classifier. Experiments on two standard classification benchmarks under different few-shot settings demonstrate the efficacy of the proposed method.