| 基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演 |
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| 引用本文： | 谭琨,张倩倩,曹茜,杜培军.基于粒子群优化支持向量机的矿区土壤有机质含量高光谱反演[J].地球科学,2015,40(8):1339-1345. |
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| 作者姓名： | 谭琨 张倩倩 曹茜 杜培军 |
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| 作者单位： | 1.中国矿业大学江苏省资源环境信息工程重点实验室, 江苏徐州 221116 |
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| 基金项目： | 国家自然科学基金项目41471356国家自然科学基金项目41402293卫星测绘技术与应用测绘地理信息局重点实验室项目KLAMTA-201410国家高技术研究发展计划(863计划)项目2008AA121100国家高技术研究发展计划(863计划)项目2012AA12A308 |
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| 摘 要： | 为了监测复垦矿区土壤的有机质含量, 综合利用光谱分析、统计学习理论与方法以及智能优化理论与方法, 研究了矿区复垦土壤有机质含量与土壤光谱之间的关系, 在此基础上建立了土壤有机质含量高光谱反演模型, 实现土壤有机质含量定量检测.首先对原始土壤光谱数据进行预处理, 然后进行相关性分析, 提取450 nm、500 nm、650 nm、770 nm、1 460 nm和2 140 nm作为特征波段, 最后利用多元线性回归(multiple linear regression, MLR)、偏最小乘回归(partial least squares regression, PLSR)和粒子群优化支持向量机回归(particle swarm optimization support vector machine regression, PSO-SVM)方法建立了土壤有机质含量的高光谱定量反演模型, 并对模型进行验证.3种模型的验证结果如下: MLR、PLSR和PSO-SVM模型的R2分别为0.79、0.83和0.85, RMSE分别为5.26、4.93和4.76.实验结果表明, 无论从模型的稳定性还是预测能力上, PSO-SVM都要优于其他两个模型.
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| 关 键 词： | 土壤有机质 高光谱 遥感 粒子群优化支持向量机 粒子群算法 |
| 收稿时间： | 2015-04-15 |
Hyperspectral Retrieval Model of Soil Organic Matter Content Based on Particle Swarm Optimization-Support Vector Machines |
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| Abstract: | To monitor the soil organic matter in the reclamation area of coal mines, the relationship between soil organic matter content and soil spectra in the reclamation area of coal mines was studied, and a quantitative retrieval model was established and validated in order to implement the organic matter content detection in this paper. After the preprocessing of the original spectral, the correlation of the organic matter content and reflectance spectra was analyzed, and 450 nm, 500 nm, 650 nm, 770 nm, 1 460 nm and 2 140 nm wavelength were extracted as feature bands. Using the multiple linear regression (MLR), partial least squares regression (PLSR) and particle swarm optimization support vector machine regression (PSO-SVM) methods, the hyperspectral quantitative retrieval models for soil organic matter content were built. The results show the coefficient of determination (R2) of MLR, PLSR and PSO-SVM were 0.79, 0.83 and 0.85 respectively, and the root mean square error of prediction (RMSEP) were 5.26, 4.93 and 4.76 respectively. The results demonstrate that the stability and predictive ability of PSO-SVM model are better than those of the MLR and PLSR model. |
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