Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs

Hyperspectral imagery (HSI) clustering aims to assign pixel-wise data with large amount of spectral bands into different groups, where each group indicates one of land-cover objects existed in HSI. Without available label information in clustering task, the clustering performance heavily depends on the reliability of unsupervised feature learned from HSI. Nevertheless,when HSI data are corrupted with noise,the conventional feature learning methods often failed. To address this problem, in this paper, a dual graph-based robust unsupervised feature extraction framework for HSI is proposed to realize reliable clustering. Firstly, low-rank reconstruction and projected learning are incorporated into the proposed framework to improve the data quality and obtain their robust structures. Then, a novel learning schemes is designed to learn two reliable graphs from the above robust structures respectively. We show that the scheme can reveals the latent similarity relationships while removing the noise influence. Meanwhile, the two reliable graphs are also integrated into a comprehensive graph with consistent constraint. At last, a joint learning framework is proposed, in which the data quality improvement, reliable graphs and consistent graph are learned iteratively to benefit from each other. After that, the normalized cut technique is applied to the learned consistent graph to obtain the final unsupervised feature. Several experiments are conducted on the two public HIS datasets to show advantage of our proposed method against the existing methods.