Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

• 作者: Zhen Ye Shihao Shi Zhan Cao Lin Bai Cuiling Li Tao Sun Yongqiang Xi
• 来源期刊: 北京理工大学学报（英文版）
• 年，卷（期）: 2021,（2）
• 页码范围: 91-112
• 页数: 22页
• 语种: 英文
• DOI: 10.15918/j.jbit1004-0579.2021.012

摘要

Hyperspectral image (HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensional-ity reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coeffi-cients as graph weights to effectively reduce reconstruction errors and represent most important in-formation of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed. Finally, the future development of this research field is prospected.