Is Meta-Learning Effective for Few-Shot Hyperspectral Image Classification?

Recently, there has been a surge of meta-learning-based approaches for the few-shot hyperspectral image classification (FSHSIC) task. Meta-learning leverages prior knowledge to teach a base-learner how to adapt quickly to a new few-shot task, which hinges on the consistency of the prior and new tasks to guarantee validity. However, hyperspectral image classification (HSIC) is an environment-dependent task, which means that the hyperspectral features of two objects in the same category can be essentially distinctive in different environments. Consequently, whether meta-learning is a feasible solution for FSHSIC is an imperative problem to investigate, notwithstanding the promising performance shown in previous meta-learning-based approaches. To this end, this work proposes a simple multilayer perceptron (MLP)-based model named SimHSIC for FSHSIC. SimHSIC utilizes only a few labeled samples from the target HSI to train the model rapidly. Surprisingly, SimHSIC outperforms existing meta-learning-based approaches, which are built upon complex three-dimensional convolutions or transformers and need heavy training processes, in prevailing public benchmarks. On the basis of extensive experiments, we conclude that the relatively better classification performances of meta-learning-based FSHSIC solutions are due mainly to the patching of each HSI pixel with the large surroundings instead of meta-learning. The code is released on https://github.com/OrigamiSL/SimHSIC.