Multi-view adaptive disagreement based active learning for hyperspectral image classification

Abstract

A multi-view based active learning method (AMD WVE ) is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. The method explores the intrinsic multi-view information embedded in hyperspectral data. By adaptively and quantitatively measuring the disagreement level of different views, the learner focuses on samples with higher confusion, rapidly reducing the version space and improving the learning speed. Classification confidence of each view towards each class is also obtained in the learning process and used to compensate for view insufficiency. Experiments show excellent performance on both unlabeled and unseen data from two sets of hyperspectral image data with 10 classes acquired by AVIRIS, as compared to random sampling and the state-of-the-art SVM SIMPLE .