为探测土壤表面干缩裂隙的发育情况,该研究以农田土壤表面干缩裂隙作为研究对象,以高密度电阻率法为测量手段,采用耦合层次聚类分析的同步连续线性估计算法(Simultaneous successive linear estimation,Sim SLE)反演土壤表面干缩裂隙的...为探测土壤表面干缩裂隙的发育情况,该研究以农田土壤表面干缩裂隙作为研究对象,以高密度电阻率法为测量手段,采用耦合层次聚类分析的同步连续线性估计算法(Simultaneous successive linear estimation,Sim SLE)反演土壤表面干缩裂隙的形态,结合试验裂隙图像和反演得到的模拟图像,对比分析裂隙形态的成像结果。研究表明:基于SimSLE算法反演得到的模拟裂隙图像,可以准确捕捉到裂隙的形状和位置,试验与模拟图像的面积、长度及欧拉数密度一致性指标均大于0.9,决定系数R^(2)都大于0.6,均方根误差数值较小。引入了层次聚类分析后,通过不断更新分区均值和边界,能够更准确地得到农田土壤干缩裂隙的形态特征,分区后模拟结果较好。引入聚类分区后Minkowski密度的决定系数R^(2)提高了9.76%~18.5%,一致性指标提高了0.93%~1.47%,均方根误差降低了12.1%~21.1%,有效提高了模拟精度。研究可为探究非侵入性探测裂隙的形态特征提供算法参考。展开更多
DEM地表形态精度分析理论与方法的建立,对DEM数据的生产和广泛应用具有重要意义。本文从局地坡面形态的凸凹性角度,剖析规则格网DEM格网点位置、格网分辨率对DEM局地坡面凸凹性的影响,以期进一步完善和发展DEM质量分析的理论与方法。论...DEM地表形态精度分析理论与方法的建立,对DEM数据的生产和广泛应用具有重要意义。本文从局地坡面形态的凸凹性角度,剖析规则格网DEM格网点位置、格网分辨率对DEM局地坡面凸凹性的影响,以期进一步完善和发展DEM质量分析的理论与方法。论文首先阐述了DEM局地坡面凸凹性的基本概念,研究建立了规则格网DEM的局地坡面凸凹性量化分析方法,并以黄土丘陵5、10、15、25、……、155 m DEM为例,采用比较分析方法研究了局地坡面凸凹性随DEM格网点位置和格网分辨率的变化特征。研究表明:对于本研究中的1:5万DEM,10 m(跃变率≤0.3%)是其最佳的格网分辨率阈值,当DEM实际格网分辨率高于该阈值时,实际DEM与最佳格网分辨率DEM具有近乎相同的局地坡面凸凹性,主要在正地形与负地形的过渡区域会发生不同程度的坡面凸凹性变化;当DEM实际格网分辨率低于该阈值时,实际DEM的局地坡面凸凹性,会随着DEM格网点布设位置和DEM格网分辨率发生较大的不确定性变化。展开更多
Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extracti...Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing(GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.展开更多
文摘为探测土壤表面干缩裂隙的发育情况,该研究以农田土壤表面干缩裂隙作为研究对象,以高密度电阻率法为测量手段,采用耦合层次聚类分析的同步连续线性估计算法(Simultaneous successive linear estimation,Sim SLE)反演土壤表面干缩裂隙的形态,结合试验裂隙图像和反演得到的模拟图像,对比分析裂隙形态的成像结果。研究表明:基于SimSLE算法反演得到的模拟裂隙图像,可以准确捕捉到裂隙的形状和位置,试验与模拟图像的面积、长度及欧拉数密度一致性指标均大于0.9,决定系数R^(2)都大于0.6,均方根误差数值较小。引入了层次聚类分析后,通过不断更新分区均值和边界,能够更准确地得到农田土壤干缩裂隙的形态特征,分区后模拟结果较好。引入聚类分区后Minkowski密度的决定系数R^(2)提高了9.76%~18.5%,一致性指标提高了0.93%~1.47%,均方根误差降低了12.1%~21.1%,有效提高了模拟精度。研究可为探究非侵入性探测裂隙的形态特征提供算法参考。
文摘DEM地表形态精度分析理论与方法的建立,对DEM数据的生产和广泛应用具有重要意义。本文从局地坡面形态的凸凹性角度,剖析规则格网DEM格网点位置、格网分辨率对DEM局地坡面凸凹性的影响,以期进一步完善和发展DEM质量分析的理论与方法。论文首先阐述了DEM局地坡面凸凹性的基本概念,研究建立了规则格网DEM的局地坡面凸凹性量化分析方法,并以黄土丘陵5、10、15、25、……、155 m DEM为例,采用比较分析方法研究了局地坡面凸凹性随DEM格网点位置和格网分辨率的变化特征。研究表明:对于本研究中的1:5万DEM,10 m(跃变率≤0.3%)是其最佳的格网分辨率阈值,当DEM实际格网分辨率高于该阈值时,实际DEM与最佳格网分辨率DEM具有近乎相同的局地坡面凸凹性,主要在正地形与负地形的过渡区域会发生不同程度的坡面凸凹性变化;当DEM实际格网分辨率低于该阈值时,实际DEM的局地坡面凸凹性,会随着DEM格网点布设位置和DEM格网分辨率发生较大的不确定性变化。
基金supported by the National Natural Science Foundation of China(No.61275010)the PhD Programs Foundation of Ministry of Education of China(No.20132304110007)
文摘Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing(GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.
