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浙江大学学报（农业与生命科学版）
浙江大学学报(农业与生命科学版)  2017, Vol. 43 Issue (2): 211-219    DOI: 10.3785/j.issn.1008-9209.2016.08.021
植物保护     
结合HJ卫星影像和最小二乘孪生支持向量机的小麦蚜虫遥感监测
胡根生1,2，吴问天1,2,3，罗菊花4，黄文江1,2,3*，梁栋1,2，黄林生1,2
1.安徽大学安徽省农业生态大数据工程实验室，合肥230601；
2.安徽大学电子信息工程学院，合肥230601；
3.中国科学院遥感与数字地球研究所，数字地球重点实验室，北京100094；
4.中国科学院南京地理与湖泊研究所，南京210008
 Remote sensing monitoring of wheat aphids by combining HJ satellite images with least square twin support vector machine model.
HU Gensheng1, 2, WU Wentian1, 2, 3, LUO Juhua4, HUANG Wenjiang1, 2, 3*, LIANG Dong1, 2, HUANG Linsheng1, 2
（1. Anhui Engineering Laboratory of Agro- Ecological Big Data, Anhui University, Hefei 230601, China; 2. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; 3. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 4. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China）
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摘要： 为了准确、及时地监测小麦蚜虫发生情况，利用野外定位调查数据及环境与灾害监测预报小卫星星座HJCCD 和HJ-IRS影像数据，在北京市通州区和顺义区小麦蚜虫发生的关键生育期（灌浆期），提取对蚜虫病情影响较大的小麦长势因子和生境因子，利用最小二乘孪生支持向量机建立该研究区的小麦蚜虫监测模型，并与传统支持向量机、费歇尔线性判别分析和学习矢量量化神经网络模型的监测结果进行对比。结果表明：最小二乘孪生支持向量机模型的总体监测精度达到86.4%，优于传统支持向量机模型（77.3%）、费歇尔线性判别分析模型（77.3%）和学习矢量量化神经网络模型（72.7%），取得了较好的监测效果。
Abstract: Pests and diseases have become serious because of global warming, which have caused great economic losses to agricultural production, and have threatened human life and health, so it was very urgent and challenging to prevent or control pests and diseases. Real-time dynamic monitoring of the occurrence of pests and diseases in large scale continuous space can guide the prevention or control work accurately and effectively to reduce the impact of pests and diseases as well as the environmental pollution caused by the indiscriminate use of pesticides. Remote sensing technology can provide effective information for crop pests and diseases monitoring quickly and accurately on a massive continuous spatial surface. HJ- 1A/1B satellite has a high revisit period（4 days）. Multi- spectral images obtained by HJ- 1A/1B satellite sensors have high spatial resolution（30 m）and are very suitable for the monitoring of agricultural pests and diseases. The occurrence of wheat aphids seriously affects the yield and quality of wheat. Monitoring of the wheat aphids accurately and timely is helpful for effective prevention and control of pests. In this paper, by using the field location survey data and the HJ-CCD and HJ-IRS image data, the growth factors and the environmental factors of wheat are extracted, including normalized difference vegetation index（NDVI）, green normalized difference vegetation index（GNDVI）, reflectance of red band, land surface temperature（LST）and perpendicular drought index（PDI）. These factors had a great influence on the occurrence of wheat aphids. The monitoring model of wheat aphids in Tongzhou District and Shunyi District of Beijing was established by using the least square twin support vector machine（LSTSVM）. The LSTSVM has a good processing ability for large scale unbalanced data and has stronger robustness than the traditional support vector machine（SVM）. Computational complexity of LSTSVM is reduced by using the least squares algorithm to transform inequality constraints into equality constraints. Experimental results showed that: the overall monitoring accuracy of the LSTSVM model was 86.4% and the Kappa coefficient was 0.71; the traditional SVM model was 77.3% and 0.52; the Fisher linear discriminant analysis（FLDA）model was 77.3% and 0.54; and the learning vector quantization（LVQ）neural network model was 72.7% and 0.39. In sum, the algorithm proposed in this paper has higher precision than the traditional SVM, FLDA and LVQ neural network.
出版日期: 2017-03-25
CLC:  TP 79  
基金资助:  国家自然科学基金（61672032，41271412）；安徽省自然科学基金（1408085MF121，1608085MF139）；安徽省科技计划项目（16030701091，1604A0702016）；中国科学院国际合作局对外合作重点项目（131211KYSB20150034）
通讯作者: 黄文江（http://orcid.org/0000-0003-1710-8301），E-mail: huangwj@radi.ac.cn   
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胡根生
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引用本文:

胡根生，吴问天，罗菊花，黄文江，梁栋，黄林生. 结合HJ卫星影像和最小二乘孪生支持向量机的小麦蚜虫遥感监测[J]. 浙江大学学报(农业与生命科学版), 2017, 43(2): 211-219.

HU Gensheng, WU Wentian, LUO Juhua, HUANG Wenjiang, LIANG Dong, HUANG Linsheng.  Remote sensing monitoring of wheat aphids by combining HJ satellite images with least square twin support vector machine model.. Journal of Zhejiang University (Agriculture and Life Sciences), 2017, 43(2): 211-219.

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http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2016.08.021    或    http://www.zjujournals.com/agr/CN/Y2017/V43/I2/211

 
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