改进的Malkmus带模式晴空大气透射率计算方法(英文)

Malkmus带模式在气候研究以及遥感应用中具有较高的应用价值,然而其计算精度不高。针对该问题,引入修正量对Malkmus模型进行改进。实验采用HITRAN2008谱线数据和美国空军地球物理实验室(AFGL)提供的6种大气模式计算垂直路径大气分子的透射率数据。在不同的大气模式下,以MODTRAN计算结果为参考标准,利用最小二乘拟合得到修正量并将其用于大气透射率补偿。相对于传统Malkmus模型,改进模型的透射率精度提高了1~2个数量级。结果表明,本方法不仅能有效地提高模型计算精度,而且适用于不同的大气模式和大气分子。     

基于连续GPS跟踪站资料的SRTM高程精度评估和验证

采用南加州地区294个连续GPS观测站的高程资料,本文对该地区的SRTM高程精度进行了评估和验证。结果显示,经过大地水准面差距补偿后（GPS和SRTM分别采用参考椭球面和EGM96大地水准面作为高程基准）,SRTM和GPS高程的差异非常小,基本上在0附近波动。高程差异小于10m、20m、30m的点的比例分别达82．7％、93．9％和98．3％。差异的均值为-4．28m,差异的标准偏差为±8．63m。因此该地区的SRTM高程精度优于标称的±16m。     

一种修正的Goldstein干涉图滤波方法

干涉图的相位噪声是相干性和视数的函数。因此,一个好的干涉图滤波器应同时考虑相干性和视数的影响。本文对在干涉图滤波中广泛应用的Goldstein滤波器进行改进,通过视数的引入,对滤波器的滤波参数进行修正,大大增强了滤波的自适应性。模拟数据和真实数据的验证说明新的滤波器在滤波效果上有明显的改善,具有更好的稳健性。     

《测量学基础》课程考核改革研究与实践

文中通过对《测量学基础》课程综合考核的改革研究与探索,结合对该课程的授课实践,并依据其在目前课程考核中还存在的问题,从考核内容和考核方式的改革、理论与实践相结合,赛考结合等方面进行了教学方法探讨,旨在促进课程考核体系整体提升,使实验室与野外实习基地建设以及师资队伍建设等各方面都有较大提高,激发老师和学生上课的积极性,进而使教学质量不断提高。     

PALSAR可检测的最大／最小地表形变梯度的统一函数模型

基于相干性和离散视数,建立PALSAR干涉可检测的最大和最小形变梯度的经验函数模型。在此基础上,采用最小二乘回归的方法拟合模型系数,建立关于相干性和视数两个参数的统一函数模型。利用汶川地区的PALSAR数据进行实验,结果表明该模型能够准确判别地表变形能否被D．InSAR技术检测到。研究结果可以帮助研究人员准确选择PALSAR数据和合理配置其干涉处理参数,对解释地表形变结果也具有重要的价值。     

Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

The general regression neural network（GRNN） model was proposed to model and predict the length of day（LOD） change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum（AAM） function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.     

Hybrid grey model to forecast monitoring series with seasonality

The grey forecasting model has been successfully applied to many fields. However, the precision of GM（1,1） model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM （1,1） model, the forecasting series of GM（1,1） was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i. e. , seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM（1,1） model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM（1,1） model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM（1,1） model. The mean absolute error and mean square error of seasonal index-GM（1,1） are 30.69 % and 54.53 % smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.     

PALSAR可检测的最大/最小地表形变梯度的统一函数模型（英文）

基于相干性和离散视数,建立PALSAR干涉可检测的最大和最小形变梯度的经验函数模型。在此基础上,采用最小二乘回归的方法拟合模型系数,建立关于相干性和视数两个参数的统一函数模型。利用汶川地区的PALSAR数据进行实验,结果表明该模型能够准确判别地表变形能否被D-InSAR技术检测到。研究结果可以帮助研究人员准确选择PALSAR数据和合理配置其干涉处理参数,对解释地表形变结果也具有重要的价值。