Accessibility-Free Active Learning For Hyperspectral Image Classification

This work proposes a new collaborative active and semi-supervised learning approach, named accessibility-free active learning (AFAL), for hyperspectral imaging classification. The proposed approach aims to tackle an existing problem in traditional active learning methods, that is, the fact that some selected samples are not accessible by oracles for assigning them pseudo labels, i.e., confident predictions for the classifier. The proposal specifically addresses this problem using superpixels in a self-training context. Specifically, AFAL first generates a set of candidates locally around the labeled pixels and then expands them to other subregions via a density peak-based augmentation strategy, in order to guarantee the confidence of pseudo labels. Our experimental results, obtained on two real and well-used hyperspectral images, reveal that the proposed scheme can lead to state-of-the-art performance.