Multiscale Superpixel-Based Active Learning for Hyperspectral Image Classification

This letter proposes a novel active learning (AL) framework that utilizes the information derived from multiscale superpixel maps for the classification of hyperspectral image. Considering that the nearby pixels with similar spectral properties tend to belong to the same class, we introduce the multiscale superpixel maps for the automatic labeling of the selected informative samples. Moreover, to exploit the multiscale characteristics of objects in the image, a hierarchical fusion approach is developed to integrate the spatial information provided by the superpixel maps into the classification result. To illustrate the effectiveness of the proposed AL framework, experiments on a series of hyperspectral images are conducted and analyzed. The results confirm the superiority of the proposed method compared to the other algorithms.