Semisupervised graph-based hyperspectral images classification using low-rank representation graph with considering the local structure of data

Seyyed Ali Ahmadi; Nasser Mehrshad; Seyyed Mohammad Razavi

doi:10.1117/1.JEI.27.6.063002

13 November 2018 Semisupervised graph-based hyperspectral images classification using low-rank representation graph with considering the local structure of data

Seyyed Ali Ahmadi, Nasser Mehrshad, Seyyed Mohammad Razavi

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063002 (November 2018). https://doi.org/10.1117/1.JEI.27.6.063002

Abstract

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.

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Seyyed Ali Ahmadi, Nasser Mehrshad, and Seyyed Mohammad Razavi "Semisupervised graph-based hyperspectral images classification using low-rank representation graph with considering the local structure of data," Journal of Electronic Imaging 27(6), 063002 (13 November 2018). https://doi.org/10.1117/1.JEI.27.6.063002

Received: 30 April 2018; Accepted: 12 September 2018; Published: 13 November 2018

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