سابِق|تال

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

ملخص

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.

مفهوم

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

References

  1. 1.
    Chang C C and Lin C J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3): 1-27
  2. 2.
    Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429
  3. 3.
    Cheng P and Wang X L. 2011. Influence of SVR parameter on non-linear function approximation. Computer Engineering, 37(3): 189-191, 194
  4. 4.
    Cherkassky V. 1997. The nature of statistical learning theory. IEEE Transactions on Neural Networks, 8(6): 1564
  5. 5.
    Dorigo M and Gambardella L M. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1): 53-66
  6. 6.
    Durbha S S, King R L and Younan N H. 2007. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing of Environment, 107(1/2): 348-361
  7. 7.
    Fang X Q and Zhang W C. 2003. The application of remotely sensed data to the estimation of the leaf area index. Remote Sensing for Land and Resources, 15(3): 58-62
  8. 8.
    Forsati R, Moayedikia A, Keikha A and Shamsfard M. 2012. A novel approach for feature selection based on the bee colony optimization. International Journal of Computer Applications, 43(8): 13-16
  9. 9.
    Ghamisi P, Couceiro M S and Benediktsson J A. 2015. A novel feature selection approach based on FODPSO and SVM. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2935-2947
  10. 10.
    Guo L, Pei Z Y, Zhang S L, Sun J Y, Liang Z L and Teng D J. 2010. Estimation method of sugarcane leaf area index using HJ CCD images. Transactions of the CSAE, 26(10): 201-205
  11. 11.
    He Y Z, Zhang Z Q and Guan C Y. 2015. Application and prospect of hyperspectral remote sensing technology in precision agriculture monitoring. Crop Research, 29(1): 96-100
  12. 12.
    Holland J H. 1975. Adaption in Natural and Artificial Systems. Ann Arbor: University of Michigan Press
  13. 13.
    Karaboga D. 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Erciyes University
  14. 14.
    Karaboga D and Akay B. 2009. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1): 108-132
  15. 15.
    Karaboga D and Basturk B. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39(3): 459-471
  16. 16.
    Karaboga D, Gorkemli B, Ozturk C and Karaboga N. 2014. A comprehensive survey: Artificial Bee Colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1): 21-57
  17. 17.
    Kennedy J and Eberhart R. 1995. Particle swarm optimization//Proceedings of 1995 IEEE International Conference on Neural Networks. Perth, WA, Australia: IEEE: 1942-1948
  18. 18.
    Kwok J T Y. 1998. Support vector mixture for classification and regression problems//Proceedings of the 14th International Conference on Pattern Recognition. Brisbane, Queensland: IEEE
  19. 19.
    Latifi H and Galos B. 2010. Remote sensing-supported vegetation parameters for regional climate models: a brief review. iForest-Biogeosciences and Forestry, 3(4): 98-101
  20. 20.
    Li N, Zhu X F, Pan Y Z and Zhan P. 2018. Optimized SVM based on artificial bee colony algorithm for remote sensing image classification. Journal of Remote Sensing, 22(4): 559-569
  21. 21.
    Liang D, Guan Q S, Huang W J, Huang L S and Yang G J. 2013. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat. Transactions of the Chinese Society of Agricultural Engineering, 29(7): 117-123
  22. 22.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
  23. 23.
    Liu X C, Fan W J, Tian Q J and Xu X R. 2008. Comparative analysis among different methods of leaf area index inversion. Acta Scientiarum Naturalium Universitatis Pekinensis, 44(2): 827-834
  24. 24.
    Liu Y, Liu R G, Chen J M, Cheng X and Zheng G. 2013. Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing. Journal of Geo-information Science. 15(05): 734-743
  25. 25.
    Steinbrunn M, Moerkotte G and Kemper A. 1997. Heuristic and randomized optimization for the join ordering problem. The VLDB Journal, 6(3): 191-208
  26. 26.
    Tuia D, Verrelst J, Alonso L, Perez-Cruz F and Camps-Valls G. 2011. Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters, 8(4): 804-808
  27. 27.
    Wang D C, Fang T J, Tang Y and Ma Y J. 2003. Review of support vector machines regression theory and control. Pattern Recognition and Artificial Intelligence, 16(2): 192-197
  28. 28.
    Wang L, Zhang Y, Peng W H, Xu B and Wang Q C. 2014. SVR approach based on artificial bee colony optimization. Systems Engineering and Electronics, 36(2): 326-330
  29. 29.
    Wu X X, Xie Q Y. 2014. Research progress on inversion methods of leaf area index use remote sensing.China Agriculture Information. (07): 285-286
  30. 30.
    Wang Y F and Zheng X J. 2010. Sensitivity analysis of model parameters and υ-SVR model of slope deformation due to excavating from open-pit to underground mining. Chinese Journal of Rock Mechanics and Engineering, 29(S1): 2902-2907
  31. 31.
    Xia T, Wu W B, Zhou Q B and Zhou Y. 2013. Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 29(3): 139-147
  32. 32.
    Xing Z R, Feng Y G, Li W M, Wang P and Yang G J. 2010. The research status of inversion of leaf area index with hyperspectral remote sensing. Science of Surveying and Mapping, 35(S1): 162-164, 62
  33. 33.
    Yan G H and Zhu Y S. 2009. Parameters selection method for support vector machine regression. Computer Engineering, 35(14): 218-220
  34. 34.
    Yang M, Lin J, Gu Z Y, Tong G C, Wong Y B, Zhang J C and Lu X Z. 2015. Leaf area index retrieval based on Landsat 8 OLI multi-spectral image data and BP neural network. Science of Soil and Water Conservation, 13(4): 86-93
  35. 35.
    Yang M, Liu Y and Kong B. 2009. Research on parameters selection method of SVR model. Computer ERA, (11): 53-55
  36. 36.
    Yu M and Ai Y Q. 2012. SVM parameter optimization and application based on artificial bee colony algorithm. Journal of Optoelectronics·Laser, 23(2): 374-378
  37. 37.
    Zai S M, Wen J, Guo D D, Han Q B, Deng Z, Sun H and Zhao D B. 2011. Determination of leaf area of sweet pepper based on support vector machine model and image processing. Transactions of the Chinese Society of Agricultural Engineering, 27(3): 237-241
  38. 38.
    Zang S Y, Zhang C, Zhang L J and Zhang Y H. 2012. Wetland remote sensing classification using support vector machine optimized with genetic algorithm: a case study in Honghe nature national reserve. Scientia Geographica Sinica, 32(4): 434-44

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