A Novel Hyperspectral Image Clustering Method With Context-Aware Unsupervised Discriminative Extreme Learning Machine - Jinhuan Xu (IEEE Access, 2018)
The extension of supervised extreme learning machine (ELM) to unsupervised one, which involves discriminative and manifold regularization, is increasingly gaining attention in hyperspectral image (HSI) clustering. This is due to the fact that HSI clustering problem requires a spectral-spatial feature extraction mechanism that must fully exploit local spectral-spatial contexts and global discriminative information to reduce the misclassification while improve the robustness in clustering procedural. In this paper, we propose a novel context-aware unsupervised discriminative ELM method for HSI clustering. The main novelty of the proposed method are twofold:1) a local spectral-spatial context integration and reshaping mechanism is incorporated into the hidden layer feature representation by using a context-aware propagation filtering procedure; and 2) both local manifold and global discriminative regularization are integrated into unsupervised ELM framework to learn an effective data representation. The most important advantage of the proposed method is that it efficiently exploits the spatial contextual information of HSI through a propagation filtering procedural; furthermore, the learned data representation can capture the intrinsic structure by exploiting the local manifold and global information by discriminative regularization. Experimental results show that the proposed algorithm obtains a competitive performance and outperforms other state of the art ELM-based methods and the other unsupervised methods.