��������ϵ��GNSS�����������ת�������о�

??GNSS??????????У??轫GNSS?????????????????????????????????????????GNSS???????????????????????????????????????????????????????????????????????????????????????????????????????????????е???????????????????????е????λ???ó????????????????????????????????????????????????????????????????о???GNSS????????????????????????????????????????????????????????????????????????????????????????????????????????????     

������Ƕ�Ȩ�ĵ������ݼ�Ȩ������С����ƽ������о�

?????????????????λ???????????????????????????????????С????????????????÷?????????????С??????????????????????????????????????и?????????????????????????????????????????3????????????????????????????????????С?????????????С??????????÷??????????????????????     

Selection of Less Biased Threshold Angles for SAM Classification Using the Real Value–Area Fractal Technique

The accuracy of classification of the Spectral Angle Mapping (SAM) is warranted by choosing the appropriate threshold angles, which are normally defined by the user. Trial‐and‐error and statistical methods are commonly applied to determine threshold angles. In this paper, we discuss a real value–area (RV–A) technique based on the established concentration–area (C–A) fractal model to determine less biased threshold angles for SAM classification of multispectral images. Short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used over and around the Sar Cheshmeh porphyry Cu deposit and Seridune porphyry Cu prospect. Reference spectra from the known hydrothermal alteration zones in each study area were chosen for producing respective rule images. Segmentation of each rule image resulted in a RV–A curve. Hydrothermal alteration mapping based on threshold values of each RV–A curve showed that the first break in each curve is practical for selection of optimum threshold angles. The hydrothermal alteration maps of the study areas were evaluated by field and laboratory studies including X–ray diffraction analysis, spectral analysis, and thin section study of rock samples. The accuracy of the SAM classification was evaluated by using an error matrix. Overall accuracies of 80.62% and 75.45% were acquired in the Sar Cheshmeh and Seridune areas, respectively. We also used different threshold angles obtained by some statistical techniques to evaluate the efficiency of the proposed RV–A technique. Threshold angles provided by statistical techniques could not enhance the hydrothermal alteration zones around the known deposits, as good as threshold angles obtained by the RV–A technique. Since no arbitrary parameter is defined by the user in the application of the RV‐A technique, its application prevents introduction of human bias to the selection of optimum threshold angle for SAM classification.     

相干能量梯度与属性比例融合在断裂构造解释中的应用

相干能量梯度体现了相干数据的振幅变化,它比较有利于突出一些细小的地质特征变化,其属性比例融合是将两个数据体按照一定比例融合在一起生成新的数据体,而新数据体能更好的反映地质特征。实例表明,与相干属性相比,相干能量梯度属性对一些细小构造更敏感,而且可以根据不同方向、时间的切片对不同走向的断层进行追踪;与常规地震时间剖面相比,属性比例融合后的属性剖面,可以在剖面中显示两种属性体的断裂构造特性,减少单一属性体的不确定性。     

On the topography influence on subsidence due to horizontal underground mining using the influence function method

The classical influence function method is widely used in mining subsidence prediction, but its use is typically limited to predicting the subsidence associated with horizontal stratiform underground mining when the ground surface is flat. By investigating the topography influence on subsidence under simplified mining conditions, this study attempts to improve the original influence function method to take topographic variations into account. New asymmetrical influence functions are used to simulate element subsidence. Integrating this asymmetrical approach into the influence function method increases the realism of subsidence computation when compared to the numerical simulation results. The maximum subsidence value and influence angle got from field data are introduced into the improved method, then two field subsidence cases are studied and acceptable comparison results are achieved. This improved method should serve as the foundation for future work.     

极化方位角补偿信息支持下的植被参数反演

SAR图像中散射目标的散射矩阵受极化方位角(POA)的影响会改变散射体的散射特性,散射矩阵是极化干涉SAR (PolInSAR)估计不同极化状态下复相干性的基础。本文根据极化方位角产生机制,建立了多视情况下基于极化方位角补偿的极化干涉相干性估计模型,分析了极化方位角补偿对相干性估计方法和不同散射机制下相干性估计的影响程度,研究了基于三阶段法与极化方位角补偿的植被参数反演。利用L波段SIRC全极化SAR图像为实验数据验证极化方位角补偿对极化干涉相干性估计和植被参数反演的可行性。实验结果表明,极化方位角补偿能够改变不同极化状态相干性分布规律,提高相干直线拟合精度,改善植被参数反演的可靠性和合理性。     

巧用GIS空间叠置分析计算遮蔽角

雷达在某个方向上发现目标的最小高低角叫遮蔽角[1]。快速准确计算出气象雷达各方向的最大遮蔽角能够为雷达站的点位和天线高度提供设计依据。本文以某机场为例,在不考虑植被或建筑物高度的提前下,利用等高线数据,采用GIS的空间叠置分析功能快速计算该机场气象雷达每隔2。5°方向的最大遮蔽角。相比传统的断面计算法,GIS空间叠置分析方法大大提高了工作效率,希望能够引导读者注重GIS在工作中的应用。     

旋转矩阵表达方法对相机检校的影响

在大倾角像片的相机检校中,经典共线方程是采用欧拉角（妒,纠,Ⅳ）描述旋转矩阵的,该方法受限于无法获取位置与姿态的初始值,结果可能导致迭代不收敛。共线方程中的旋转矩阵还可以直接利用方向余弦或单位四元数来描述,相机检校时可以无需依赖位置与姿态的初始值。在相同实验数据、初始值和收敛条件的实验中,直接利用方向余弦描述旋转矩阵的方法明显优于单位四元数方法,主要体现在收敛情况和计算结果接近欧拉角方法两个方面。建议在非量测相机检校时,最好选用直接利用方向余弦描述旋转矩阵的共线方程方法。     

重金属铜污染植被光谱响应特征研究

重金属铜污染植被的反射光谱特性会发生明显改变。在本研究中,采用不同程度的铜污染土壤作为培养基质,选择春小麦、上海青两种农作物进行铜胁迫实验,获取了4个不同生育期、10个不同铜污染强度下的植被叶片的反射光谱,并采用铜污染叶片7个特征波段和光谱角的方法研究了铜污染叶片的光谱特征。结果表明,铜污染叶片光谱差异与作物时期和作物类型有关,可以采用叶片光谱角描述铜污染叶片与健康叶片的光谱差异。该方法只需与阈值做简单的比较,方法简便易行,而且对轻度及重度铜污染十分敏感。叶片光谱辐射传输模型反演结果表明铜污染叶片内部结构参数N明显变大,这也证明了铜污染使叶片内部结构更加散乱无序。在此基础上进一步建立了N与红肩处反射率值的线性关系,相关系数为0.978。本文为铜污染叶片光谱反射模型的建立提供了初步的数据基础与理论支持。     

嫦娥3号巡视探测器桅杆的转动角度误差检校

嫦娥3号巡视探测器桅杆的展开机构、偏航机构和俯仰机构在转动过程中存在系统性的偏差,这些误差影响了巡视探测器导航定位、地形恢复和路径规划的精度.针对这一问题,文章提出了一种间接平差模型,通过真实数据试验证明该方法能够用于对嫦娥3号巡视探测器桅杆的转动角度误差进行检校,并且具有很高的精度和稳定性.