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Remote Sensing Technology and Application  2021, Vol. 36 Issue (3): 587-593    DOI: 10.11873/j.issn.1004-0323.2021.3.0587
    
Research on Feature Recognition Method of Hyperspectral Image based on LFDA and GA-ELM
Baoyun Li1(),Yugang Fan1,2,3(),Mingli Yang1
1.Faculty of Information Engineering & Automation,Kunming University of Science and Technology,Kunming 650500,China
2.Engineering Research Center for Mineral Pipeline Transportation,Kunming 650500,China
3.Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming 650500,China
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Abstract  

The high-dimensional characteristics of the hyperspectral image and the high correlation between the bands have led to the problem of large data volume and information redundancy in the study of the feature recognition of hyperspectral images, which reduces the classification and recognition accuracy of hyperspectral images. Aiming at the above problems, a hyperspectral image classification method based on Local Fisher Discriminant Analysis (LFDA) combined with Genetic Algorithm (GA) to optimize Extreme Learning Machine (ELM) is proposed. First, the LFDA is used to reduce the dimensionality of the hyperspectral image data to eliminate information redundancy and retain the main features in the local neighborhood; then use GA to optimize the ELM, classify the feature samples after the dimensionality reduction, and improve the classification and recognition of the hyperspectral image Precision. The method proposed in this paper is applied to the research on the feature recognition of hyperspectral images in Salinas and Pavia University. The classification accuracy reaches 98.56% and 97.11% respectively, which verifies the effectiveness of the method in this paper.

Key words:  Hyperspectral image      Dimensionality reduction      Extreme learning machine      Classification recognition     
Received:  17 December 2019      Published:  22 July 2021
ZTFLH:  TP751.1  
Corresponding Authors:  Yugang Fan     E-mail:  1475052566@qq.com;ygfan@qq.com
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Baoyun Li
Yugang Fan
Mingli Yang

Cite this article: 

Baoyun Li,Yugang Fan,Mingli Yang. Research on Feature Recognition Method of Hyperspectral Image based on LFDA and GA-ELM. Remote Sensing Technology and Application, 2021, 36(3): 587-593.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.3.0587     OR     http://www.rsta.ac.cn/EN/Y2021/V36/I3/587

Fig.1  The hyperspectral remote sensing image of Salinas
Fig.2  The hyperspectral remote sensing image of Pavia University
降维方式分类方式AA/%OA/%Kappa系数

不进行降维

SVM97.0296.870.972
ELM73.2973.110.736
GA-ELM85.5685.270.855

PCA

SVM95.1295.870.955
ELM95.6595.600.952
GA-ELM85.3785.620.863

LDA

SVM94.5695.500.958
ELM94.2295.620.962
GA-ELM86.3786.130.866

LFDA

SVM96.5696.500.958
ELM96.2296.620.962
GA-ELM97.3898.560.972
Table 1  Experimental results of Salinas dataset
降维方式分类方式AA/%OA/%Kappa系数

不进行降维

SVM96.1795.560.965
ELM53.4853.920.545
GA-ELM86.3786.670.862

PCA

SVM94.3793.670.945
ELM92.0391.040.922
GA-ELM95.8994.130.944

LDA

SVM93.3396.440.945
ELM93.1295.110.947
GA-ELM95.9295.420.956

LFDA

SVM94.5096.600.930
ELM95.5696.000.946
GA-ELM96.2397.110.964
Table 2  Experimental results of Pavia University dataset
Fig.3  Classification results of different methods in Salinas dataset
Fig.4  Classification results of different methods in Pavia University dataset
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