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Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：hjsu@hhu.edu.cn
  • Introduction：苏红军，研究方向为高光谱遥感、资源环境遥感等研究。E-mail： hjsu@hhu.edu.cn
SU Hongjun,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

YAO Wenjing,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

WU Zhaoyue

résumé

Recently, Low-Rank Representation (LRR) has been widely used in hyperspectral remote sensing imagery classification. How to accurately classify ground objects by LRR has become a challenge in hyperspectral remote sensing research. The LRR based on elastic net (ENLRR) and the extended kernel version of ENLRR (KENLRR) are proposed to solve the above-mentioned problem. LRR classification method can make full use of the global information of the image. Its basic idea is to represent the whole test image by using the linear combination of as few training samples as possible, reconstructing the target image according to the representation coefficient matrix and training samples, and calculating the class of each pixel by the minimum reconstruction error criterion. The main idea of ENLRR is to introduce an elastic net into the LRR model, which replaces the rank function with the combination of nuclear and Frobenius norms of the coefficient matrix. To better classify nonlinear data, a modified KENLRR method is proposed by introducing kernel tricks in the ENLRR algorithm, and the neighborhood filter kernel function is adopted to map the original data into a high-dimensional feature space, which can obtain spatial-spectral joint information for better classification. In the experiments, three popular hyperspectral datasets are adopted, the proposed methods and the SVM, KNN, ELM, LRR, MFLRR, LSLRR, and KLRR comparison methods are used to carry out classification. Based on the experimental results, the proposed methods are effective in accurately distinguishing ground objects and have good stability and adaptability. In comparison with LRR method, the overall classification accuracies of ENLRR and KENLRR are improved by 4.55% and 6.74% in the Washington DC dataset, 14.22% and 23.30% in the Purdue Campus dataset, and 8.45% and 15.40% in the Gaofen-5 (GF-5) Yellow River Delta dataset. Therefore, the KENLRR method can provide the best performance for hyperspectral remote sensing imagery classification. The high-quality classification results provide technical support for analyzing the distribution pattern of ground objects, and prove the superiority of the proposed methods in hyperspectral remote sensing imagery classification。

mots-clés

hyperspectral remote sensing;low-rank representation;ENLRR;KENLRR;image classification

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