Edge-Inferring Graph Neural Network With Dynamic Task-Guided Self-Diagnosis for Few-Shot Hyperspectral Image Classification

The current hyperspectral image classification (HSIC) model based on the convolutional neural network for feature extraction and softmax classifier has been prone to the barrier of label prediction with limited samples. Substituting for the enormously complicated work of terrain labeling, few-shot learning provides a popular option for HSIC with very few annotated samples. In this article, we proposed a novel edge-inferring framework with the metalearning paradigm for hyperspectral few-shot classification (HSFSC), in which a graph neural network for similarity measurement is first presented to iteratively infer edge labels with the exploitation of instance-level similarity and the distribution-level similarity. Besides, in the metatraining stage, the pixel prediction model and the patch prediction model based on edge-inferring architecture are concretized jointly to improve the classification accuracy of the test samples. Expressly, at the metatesting phase, the dynamic task-guided self-diagnosis strategy is developed for the first time to diagnose the samples separability of the current classification task, which is responsible for dynamically assigning the most reliable results based on the generated reliability grade of the sample. The extensive experimental results and analysis of three hyperspectral image datasets demonstrate the superiority of the proposed HSFSC architecture compared with other advanced methods.