Multiview Spatial–Spectral Active Learning for Hyperspectral Image Classification

Abstract

Supervised classification algorithms on the intricate ground object information of hyperspectral images (HSIs) require a large number of training samples that are annotated manually for model learning. To reduce the labeling cost and improve training sample effectiveness, a multiview spatial–spectral active learning (MVSS-AL) model is proposed in this study. First, a committee model composed of collaborative representation classification is introduced to form a leave-one-class-out (LOCO) multiview strategy, which explores more effective information in the limited training data. Second, the sample query strategy is designed from the perspective of classification confidence (CC) and training contribution (TC). The most inconsistent high-quality samples are screened by making full use of iterative prediction information and spatial–spectral features contained in hyperspectral imagery. Finally, the spatial–spectral LOCO active learning (AL) model obtains target samples through two-layer screening in each iteration and utilizes a support vector machine to obtain the final classification results. The proposed method is tested on three real-world hyperspectral datasets, and the comparison with several novel methods shows that the proposed method is better in the classification performance of restricted sample training.