Inversion of Leaf Area Index Based on Extreme Learning Machine Regression in Road Vegetation

doi:10.13474/j.cnki.11-2246.2018.0285

测绘通报 ›› 2018, Vol. 0 ›› Issue (9): 82-86.doi: 10.13474/j.cnki.11-2246.2018.0285

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Inversion of Leaf Area Index Based on Extreme Learning Machine Regression in Road Vegetation

LEI Yubin¹, ZHU Shankuan^2,3, GUO Yunkai³, LI Danna³, LIU Lei³, LIU Ning³

1. The Second Surveying and Mapping Institute of Hunan Province, Changsha 410000, China;
2. Shanghai Waterway Engineering Design and Consulting Company Limited, Shanghai 200000, China;
3. Institute of Surveying and Mapping and Remote Sensing Applied Technology, Changsha University of Science & Technology, Changsha 410076, China

Received:2018-05-22 Revised:2018-07-20 Online:2018-09-25 Published:2018-09-29
Contact: 朱善宽。E-mail:2506272955@qq.com E-mail:2506272955@qq.com

Abstract

Abstract:

The acquisition of LAI of road vegetation has an important implication for the monitoring of road vegetation growth and health status.Based on the GF-1 image data and the ground synchronous measured LAI data,the technology of extreme learning machine (ELM) was introduced to inversion modeling in the high-speed road area of Hunan Province.Compared with the traditional empirical regression and support vector machine (SVM) methods,the extreme learning machine is higher,RMSE is 0.501,and the prediction accuracy is 86.26%.The study can provide a reference for road vegetation health assessment.

Key words: remote sensing, leaf area index, inversion, GF-1 imaging, extreme learning machine

CLC Number:

P237

LEI Yubin, ZHU Shankuan, GUO Yunkai, LI Danna, LIU Lei, LIU Ning. Inversion of Leaf Area Index Based on Extreme Learning Machine Regression in Road Vegetation[J]. 测绘通报, 2018, 0(9): 82-86.

References

[1] 郭云开,张进会.路域植被叶绿素多光谱遥感定量反演研究[J].测绘通报,2014(12):5-8.
[2] 郭琳,裴志远,张松龄,等.基于环境星CCD图像的甘蔗叶面积指数反演方法[J].农业工程学报,2010,26(10):201-205.
[3] 梁栋,管青松,黄文江,等.基于支持向量机回归的冬小麦叶面积指数遥感反演[J].农业工程学报,2013,29(7):117-123.
[4] 马伟波,谭琨,李海东,等.基于超限学习机的矿区土壤重金属高光谱反演[J].生态与农村环境学报,2016,32(2):213-218.
[5] 宋英强,杨联安,冯武焕,等.基于多源辅助变量和极限学习机的蔬菜地土壤有机质预测研究[J].土壤通报,2017,48(1):118-126.
[6] HUANG G B,ZHU Q Y,SIEW C K.Extreme Learning Machine:A New Learning Scheme of Feed Forward Neural Networks[J].Proceedings IEEE International Joint Conference on Neural Networks,2004,2(2):985-990.
[7] HUANG G B,ZHU Q Y,SIEW C K.Extreme Learning Machine:Theory and Applications[J].Neurocomputing,2006,70(1):489-501.
[8] 张连蓬,柳钦火,王德高,等.高光谱遥感植被指数的普适性分析[J].测绘通报,2010(9):1-4.
[9] JORDAN C F.Derivation of Leaf Area Index from Quality of Light on the Forest Floor[J].Ecology,1969,50(4):663-666.
[10] RICHARDSON A J,WEIGAND C L.Distinguishing Vegetation from Soil Background Information[J].Photogrammetric Engineering and Remote Sensing,1977,43(12):1541-1552.
[11] ROUSE J W,HASS R H,SHELL J A,et al.Monitoring Vegetation Systems in the Great Plains with ERTS.[J].Nass Special Publication,1973,351:309.
[12] BROGE N H,LEBLANC E.Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density[J].Remote Sensing of Environment,2001,76(2):156-172.
[13] HUETE A R.A Soil-adjusted Vegetation Index (SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[14] QI J,CHEHBOUNI A,HUETE A R,et al.A Modified Soil Adjusted Vegetation Index[J].Remote Sensing of Environment,2015,48(2):119-126.

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Inversion of Leaf Area Index Based on Extreme Learning Machine Regression in Road Vegetation

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