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Optimized SVR based on artificial bee colony algorithm for leaf area index inversion

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

  • Email：201821051195@mail.bnu.edu.cn
  • Introduction：周晓雪，研究方向为农业遥感。E-mail：201821051195@mail.bnu.edu.cn
ZHOU Xiaoxue13,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

LI Nan13,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    School of Geographical Sciences, Qinghai Normal University, Xining 810016, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

  • Email：pyz@bnu.edu.cn
  • Introduction：潘耀忠，研究方向为农业遥感。E-mail：pyz@bnu.edu.cn
PAN Yaozhong123*,  
  • Affiliation：

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

SUN Lixin3

résumé

Support Vector Regression (SVR) method as a new idea in LAI inversion has certain application value and prospect. However, the value of penalty coefficient C, width parameter g of kernel function and insensitive loss function parameterεin the SVR algorithm have a significant impact on regression accuracy. This paper proposed a method for Leaf Area Index (LAI) inversion using remote sensing images based on ABC (Artificial Bee Colony) algorithm to optimize SVR parameters. In addition, the LAI measurement values were from the Soil Moisture Experiment 2002 in US (SMEX02) and Landsat 7 ETM + surface reflectance data at the same time. In order to verify the effect of SVR optimized by ABC, this paper established three types of LAI inversion models with non-optimized parameters(SVR), optimized single parameter(ABC-SVR-C, ABC-SVR-g, ABC-SVR-ε), and optimized three parameters (ABC-SVR),and compared the accuracy of the three kinds of models. Based on this, we analyzed the sensitivity of LAI inversion model of three key parameters of SVR, and did a significant test on the accuracy of the ABC algorithm optimized SVR model. The study showed: (1) Compared with the model without optimizing parameters, the four models with the SVR parameters optimized by ABC algorithm had higher accuracy, and the optimized three parameters model had better accuracy than the model with optimizing single parameter, the slope of regression straight line reaching 0.797 and decision coefficient reaching 0.775. (2) The three key parameters of SVR have an influence on the accuracy of the LAI model, and compared with the parameters C and g, the parameter ε is more uncertain to the accuracy of the model. (3) At the confidence interval of 95%, the P value of difference significance test on the slope k, r2, and RMSE between ABC-SVR model and SVR model all less than 0.005, indicated that the ABC algorithm significantly improved the accuracy of the SVR model.

mots-clés

Support Vector Regression (SVR);Artificial Bee Colony (ABC) algorithm;parameter optimization;Landsat 7;Leaf Area Index (LAI)

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