부산대학교 도서관

Because of limited labeled samples, semisupervised learning (SSL) methods have attracted much attention for classification of hyperspectral images (HSIs). Graph-based methods that treat data samples as nodes in a graph are very popular classes of SSL in the HSI data analysis. However, constructing a graph that can well capture the essential data structure is critical for these classes of SSL methods. A graph construction method based on low-rank representation (LRR) is proposed. Since LRR only captures the global structure of data, it cannot provide an informative graph for graph-based SSL tasks. To increase the effectiveness of the LRR-based graph, the local structure information is incorporated into the objective function of LRR as an additional penalty term. The proposed low-rank and local linear graph (LRLLG) takes the global and local structure into account, hence it provides a more generative and discriminative graph. Experimental results on two well-known data sets demonstrate that LRLLG outperforms the traditional graph construction methods in label propagation and graph-based SSL methods for HSIs. ® 2018 SPIEand IS&T [DOI: 10.1117/1.JEI.27.6.063002] [ABSTRACT FROM AUTHOR]