Classification of Hyperspectral Image Based on Task-Specific Learning Network

Abstract

Hyperspectral image classification, which is a crucial task for various remote sensing applications, can achieve qualified performance under a conventionalized assumption, i.e., there are sufficient samples in every concerned class. However, in field investigation, collecting enough samples for every class is extremely difficult, which results in defective training sets with insufficient or imbalanced samples and deteriorates the classification performance dramatically. In order to address this issue, we propose a hyperspectral image classification method based on a task-specific learning network. The proposed network works under an episode-based framework, which learns general knowledge from the tasks with sufficient samples by metalearning and relation learning and then inference specific knowledge for the tasks with few samples by parameters adjustment and representation comparison. In particular, a task-specific feature learner is designed to learn unbiased features, and a comparison-based classifier is utilized to adapt the minority classes. As a result, the proposed method can obtain a qualified overall accuracy over all samples with a comparative averaged accuracy over all classes. Extensive experiments on three real hyperspectral images show that our method can achieve state-of-the-art performance under both the few-shot and imbalanced training set settings.