Integrating Prototype Learning With Graph Convolution Network for Effective Active Hyperspectral Image Classification

In recent years, active learning (AL) methods have provided a feasible approach to alleviate the problem of limited labeled samples in deep learning projects. Existing AL algorithms generally tend to select sample without labeled, whose category is difficult to distinguish. However, the sample in the category center is difficult to determine in AL operations, resulting in inaccurate category measuring and inaccurate sample selection. In addition, hyperspectral images (HSIs) have rich spectral reflective bands with strong correlations, which leads to the phenomenon that the spatial distribution between different categories in HSIs characterizes staggered distribution, which undoubtedly influences the HSI classification effect. In this article, we propose a new AL method (called PLGCN) which combines prototype learning (PL) and graph convolution network (GCN) to solve few-shot HSI classification tasks, and this method can add into existing deep learning-based HSI classification models. It includes two advantages: 1) the prototype of each category is iteratively updated to ensure the optimality of prototype in each sampling stage and 2) the spatial distribution of unlabeled samples is extracted via graph convolution neural network in order to obtain the better features in new space for easier discriminating. Experimental results on three commonly used benchmark HSI datasets demonstrate the effectiveness of the PLGCN in HSI classification tasks with limited labeled samples.