Spatial-Spectral Locality-Constrained Low-Rank Representation with Semi-Supervised Hypergraph Learning for Hyperspectral Image Classification

作者：Liu, Qingshan^*; Sun, Yubao; Hang, Renlong; Song, Huihui

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(9): 4171-4182.

DOI：10.1109/JSTARS.2017.2700490

• Hyperspectral image classification
• locality constraint
• low-rank representation (LRR)
• semi-supervised hypergraph learning
• spatial-spectral similarity

In this paper, we propose a novel hyperspectral image classification method based on spatial-spectral locality-constrained low-rank representation (LRR) and semi-supervised hypergraph learning. Specifically, we first represent the hyperspectral data via LRR due to its abilities in both recovering the low-rank structure of high-dimensional observations and dealing with the noises corrupted during imaging. Then, we incorporate a locality constraint based on spatial-spectral similarity into the LRR model to further preserve the spatial information and local manifold structure. Based on LRR features, a semi-supervised hypergraph learning algorithm is designed for final classification to fully exploit the rich information of unlabeled samples. Different from the conventional pair-wise graph model, the hypergraph model can effectively capture high-order relationships among samples. Experiments are conducted on three benchmark hyperspectral datasets, and the results show that the proposed method achieves superior classification performance over other state-of-the-art methods and possesses the robustness to noise.

• 出版日期2017-9
• 单位南京信息工程大学