Local Preserving Graphs Using Intra-Class Competitive Representation for Dimensionality Reduction of Hyperspectral Image

• 作者: Zhen Ye Shihao Shi Tao Sun Lin Bai
• 来源期刊: 北京理工大学学报（英文版）
• 年，卷（期）: 2021,（2）
• 页码范围: 139-158
• 页数: 20页
• 语种: 英文
• DOI: 10.15918/j.jbit1004-0579.2021.013

摘要

As a key technique in hyperspectral image pre-processing, dimensionality reduction has received a lot of attention. However, most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures. In this paper, we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information. These two methods ex-plore local information into the collaborative graph through competing constraints, thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and en-hancing the discriminative power of the algorithm. By classifying four benchmark hyperspectral data, the proposed methods are proved to be superior to several advanced algorithms, even under small-sample-size conditions.