ALPN: Active-Learning-Based Prototypical Network for Few-Shot Hyperspectral Imagery Classification
Xiaorun Li, Zeyu Cao, Liaoying Zhao, Jianfeng Jiang
Abstract
With the development of deep learning, the benchmark of hyperspectral imagery classification is constantly improving, but there are still significant challenges for hyperspectral imagery classification of few-shot scenes. This letter proposes an active-learning-based prototypical network (ALPN), which uses the prototypical network to extract representative features from a few samples. Moreover, it combines semisupervised clustering and active learning methods to select and request labels from valuable examples actively. In this way, the feature extraction ability of the network is gradually optimized. The experimental results validated that the classification accuracy and robustness of ALPN significant exceeded the comparison baselines. Furthermore, because it can be regarded as a sample selection method, ALPN can be easily combined with other models to obtain better classification results.