《水文科学杂志》刊登“基于遗传算法的水云模型植被参数估计”

作者：Kamal Kumar, K.S. Hari Prasad, M.K. Arora

刊物：水文科学杂志，2012年，57卷第4期，776-789页

关键词：植被参数，回波系数，逆向问题，遗传算法，数据错误，偏差，均方根误差，连续无约束极小化技术

摘  要：水云模型是用来分析植被水含量对雷达回波数据的影响。该模型包含A和B两个描述植被情况的参数，以及C和D两个裸土参数。在目前的研究中，A和B两个参数估算是使用遗传算法（GA）优化技术，并与测量回波数据中的序列无约束极小化技术（SUMT）进行比较估计。参数估计是通过对ENVISAT ASAR图像校正和水云模型预测的回波系数之间的偏差使用最小二乘法得出。三个不同目标函数引起的偏差，将通过合成后回波数据进行统计学分析。观察得出，当回波系数数据无错误时，目标函数在参数估计时不会产生任何偏差，真正的参数将是唯一确定的。然而存在噪声时，这些目标函数会引起参数估计偏差。例如，基于归一化偏差的平方的目标函数同计算后回波系数一起，会得到最好的可能性评估。水云模型参数估计将使用遗传算法（GA）和序列无约束极小化技术（SUMT）对比使用得出。因此，遗传算法（GA）技术比序列无约束极小化技术（SUMT）在参数估计时的表现更好，遗传算法（GA）中的根均方差误差一半来源是序列无约束极小化技术（SUMT）。

Estimation of water cloud model vegetation parameters using a genetic algorithm

Authors：Kamal Kumar, K.S. Hari Prasad, M.K. Arora

Journal:Hydrological Sciences Journal，Volume 57, Issue 4（2012），776-789

Key words: vegetation parameter, backscatter coefficient, inverse problem, genetic algorithm, data errors, bias, root mean squared error, sequential unconstrained minimization technique

Abstract:The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT.

原文链接：http://www.tandfonline.com/doi/full/10.1080/02626667.2012.678583