Semi-Supervised Dynamic Ensemble Learning With Balancing Diversity and Consistency for Hyperspectral Image Classification

Hyperspectral coastal wetland classification requires an extensive quantity of labeled samples, which are hard to acquire. Therefore, a novel semi-supervised dynamic ensemble learning (SSDEL) framework is proposed to overcome the limitations of labeled samples in wetland hyperspectral classification. First, a collaborative relationship is established between labeled and unlabeled samples in the sample augmentation stage. Based on this relationship, unlabeled samples were assigned to the region to which the most similar samples belonged. Then, multiple classifiers are trained using labeled samples and predict unlabeled samples in the same region to obtain higher confidence pseudo-label results. Second, based on the assumption that different classifiers should produce similar classification results for a specific target sample, an objective function is designed to unify the classification behavior of multiple classifiers. The representation coefficients of multiple classifiers in the same region are constrained by optimizing the objective function through the $l_{2}$ -norm. Finally, a complete SSDEL framework is constructed by applying consistency learning again to the augmented samples. The proposed method is evaluated using three wetland hyperspectral images of China, and the experimental results demonstrate its effectiveness.