Label Propagation Ensemble for Hyperspectral Image Classification

The imbalance between limited labeled pixels and high dimensionality of hyperspectral data can easily give rise to Hughes phenomenon. Semisupervised learning (SSL) methods provide promising solutions to address the aforementioned issue. Graph-based SSL algorithms, also called label propagation methods, have obtained increasing attention in hyperspectral image (HSI) classification. However, the graphs constructed by utilizing the geometrical structure similarity of samples are unreliable due to the high dimensionality and complexity of the HSIs, especially for the case of very limited labeled pixels. Our motivation is to construct label propagation ensemble (LPE) model, then use the decision fusion of multiple label propagations to obtain pseudolabeled pixels with high classification confidence. In LPE, random subspace method is introduced to partition the feature space into multiple subspaces, then several label propagation models are constructed on corresponding subspaces, finally the results of different label propagation models are fused at decision level, and only the unlabeled pixels whose label propagation results are the same will be assigned with pseudolabels. Meanwhile extreme learning machine classifiers are trained on the labeled and pseudolabeled samples during the iteration. Compared with traditional label propagation methods, our proposed method can deal with the situation of very limited labeled samples by providing pseudolabeled pixels with high classification confidence, consequently, the accurate base classifiers are obtained. To demonstrate the effectiveness of the proposed method, LPE is compared with several state-of-the-art methods on four hyperspectral datasets. In addition, the method that only use label propagation is investigated to show the importance of ensemble technique in LPE. The experimental results demonstrate that the proposed method can provide competitive solution for HSI classification.