统计假设检验方法在全极化SAR变化检测中的应用

本文以全极化SAR数据为研究对象。由于全极化数据相干矩阵T3或协方差矩阵C3服从复wishart分布,所以首先在此分布的基础上利用统计假设检验方法构建似然比参数,用以表征地表地物的变化程度,然后利用基于广义高斯分布模型的EM迭代算法(GGM-EM)对变化信息进行初提取,最后充分考虑上下文信息,利用概率松弛迭代算法对初检测信息进行优化。该方法不仅全自动提取变化信息,而且经过非相干平均、初始分类、分类结果优化3次降斑去噪处理,因此检测精度较高。通过与传统对数比值法的比较,证明该方法的有效性。     

基于长度贝叶斯生物量估算法的北部湾带鱼资源评估

带鱼是南海西北部北部湾主要经济鱼类种群之一,估算其种群参数和评估其资源状况对北部湾渔业资源管理具有重要意义.本文利用2006-2016年北部湾带鱼(Trichiurus haumela)的生物学数据,通过长度贝叶斯生物量估算法(Length-based Bayesian Biomass Estimation Metho...     

基于ALR的Hopfield神经网络在高程拟合中的应用

运用具有正规化项的增广拉格朗日函数作为神经网络的能量函数,辅助二次曲面拟合,进一步探索Hopfield神经网络在高程拟合中的应用。实际算例表明,该方法可以大大提高神经网络的计算效率和可靠性。     

Kriging with Inequality Constraints

A Gaussian random field with an unknown linear trend for the mean is considered. Methods for obtaining the distribution of the trend coefficients given exact data and inequality constraints are established. Moreover, the conditional distribution for the random field at any location is calculated so that predictions using e.g. the expectation, the mode, or the median can be evaluated and prediction error estimates using quantiles or variance can be obtained. Conditional simulation techniques are also provided.     

带线约束的摄影测量网格变分精化算法

针对城市场景三维重建模型在有线特征的边缘区域过于平滑的问题,提出一种带线约束的摄影测量网格变分精化方法。算法以初始重建网格模型为基础,引入3个能量项,将网格精化问题转化成能量下降问题。首先结合所有的影像信息构建影像一致性约束项,然后对网格表面顶点附加正则化约束,最后引入三维线特征约束,将3个能量项加权相加后离散化到每个顶点,得到梯度变化值。采用梯度下降法,使顶点沿着梯度方向移动,当能量不再下降或迭代一定次数时,即得到了精化后的网格模型。试验结果表明,本文算法能较好地保持边缘特征,与现有的泊松重建算法相比,网格的质量更高,视觉效果更好。     

水文水资源系统贝叶斯分析现状与前景

简介了贝叶斯分析的基本原理,综述了它在水文水资源系统中考虑不确定性和风险的特点及其在径流预报、洪水分析与地区综合、水资源规划与管理等问题中的应用,并分析了其发展前景,着重指出将灰色先验分布、模糊似然函数在贝叶斯定理框架中耦合的综合途径.     

退化抛物型方程的广义T—周期解

本文证明了退化抛物型方程的边值问题     

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar,Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.