建筑区遥感观测最优尺度确定方法优化研究
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摘要
半变异函数在获取遥感影像地物最优尺度的研究中发挥着极其重要的作用。为了获取更高精度的半变异函数曲线模型的变程属性,首先定义5个影响变程精度的参数,并对其进行初始化;同时定义两个衡量参数优化结果的指标;然后利用控制变量的方法,对上述参数进行逐个优化,并利用优化后的参数获取变程值;最后,本文提出最优观测尺度的选择方法,在最优变程值的基础上,通过对影像空间结构的定量描述和Shannon采样定理,得到最优观测尺度。研究结果表明,积分变程能够较好的实现半变异函数对空间结构的定量描述,研究区遥感影像分辨率≤40 m时,能够满足观测需要。
The semivariogram has played an important role in the study of the optimal scale of Remote Sensing imagery. In order to obtain more accurate range of Semivariogram curve, we first define and initialize five parameters, which may affect its attribute values. Meanwhile, two indicators are defined to measure the optimization results of the parameters. Then the method of controlling variables is used to optimize the parameters one by one, and the optimized parameters are used to obtain the range. At the end of the paper, a method of selecting the optimal observation scale is proposed. Based on the optimal range, the optimal observation scale is obtained by quantitative description of image spatial structure and Shannon sampling theorem. The results show that Integral variation can better realize the quantitative description of spatial structure by Semivariogram. And when the resolution of the image is less than 40 m, it can meet the needs of observation.
The semivariogram has played an important role in the study of the optimal scale of Remote Sensing imagery. In order to obtain more accurate range of Semivariogram curve, we first define and initialize five parameters, which may affect its attribute values. Meanwhile, two indicators are defined to measure the optimization results of the parameters. Then the method of controlling variables is used to optimize the parameters one by one, and the optimized parameters are used to obtain the range. At the end of the paper, a method of selecting the optimal observation scale is proposed. Based on the optimal range, the optimal observation scale is obtained by quantitative description of image spatial structure and Shannon sampling theorem. The results show that Integral variation can better realize the quantitative description of spatial structure by Semivariogram. And when the resolution of the image is less than 40 m, it can meet the needs of observation.
引文
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[2] KIM J, GRUNWALD S, OSBORNE T Z, et al. Spatial Resolution Effects of Remote Sensing Images on Digital Soil Models in Aquatic Ecosystems[C]. Digital Soil Assessments and Beyond,2012.sydney,Australia.
[3] SANKEY J B, DRAUT A E. Gully Annealing by Aeolian Sediment: Field and Remote-Sensing Investigation of Aeolian-Hillslope-Fluvial Interactions, Colorado River corridor, Arizona, USA[J]. Geomorphology, 2014, 220(3):68-80.
[4] 张俊,王宝山,吕宝庆.面向对象高分辨率影像信息提取中的尺度效应研究[J].北京测绘,2010(1):11-14.
[5] 付杰,张豫.遥感数据尺度特征的研究进展[J].北京测绘,2017(1):32-34.
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[8] 韩鹏,龚健雅,李志林.基于信息熵的遥感分类最优空间尺度选择方法[J].武汉大学学报(信息科学版),2008,33(7):676-679.
[9] 柏延臣,王劲峰.基于特征统计可分性的遥感数据专题分类尺度效应分析[J].遥感技术与应用,2004,19(6):443-449.
[10] 刘玉锋,陈冬花,郑朝贵.高分辨率遥感数据云杉林林分冠幅估计[J].新疆农业大学学报,2013,36(2):153-157.
[11] 温兆飞,张树清,白静,等.农田景观空间异质性分析及遥感监测最优尺度选择--以三江平原为例[J].地理学报,2012,67(3):346-356.
[12] 薛冬冬,佘光辉,温小荣,等.基于地统计分析的南京钟山风景区景观格局尺度效应分析[J].西南林业大学学报,2012,32(1):30-35.
[13] ATKINSON P M, CURRAN P J. Choosing An Appropriate Spatial Resolution for Remote Sensing Investigations[J]. Photogrammetric Engineering & Remote Sensing,1997, 63(12):1345-1351.
[14] 张仁铎. 空间变异理论及应用[M]. 北京: 科学出版社, 2005.
[15] TARNAVSKY E, GARRIGUES S, Brown M E. Multiscale Geostatistical Analysis of AVHRR, SPOT-VGT, and MODIS Global NDVI Products[J]. Remote Sensing of Environment, 2008, 112(2):535-549.
[16] JUPP D L B, STRAHLER A H, WOODCOCK C E. Autocorrelation AndRegularization in Digital Images. I. Basic theory[J]. IEEE Transactions on Geoscience & Remote Sensing,1988, 26(4):463-473.
[17] ATKINSON P M, CURRAN P J. Defining AnOptimal Size of Support for Remote Sensing Investigations[J]. IEEE Transactions on Geoscience & Remote Sensing,1995, 33(3):768-776.
[18] JOHNSTON K, HOEF J M V, KRIVORUCHKO K, et al. Using ArcGIS Geostatistical Analyst[M]. Cinderella Manual,Redlands,Califarnia,USA:ESRI,2008.
[19] 王政权. 地统计学及其在生态学中的应用[M]. 北京: 科学出版社,1999.
[20] LANTUéJOUL C. Geostatistical Simulation: Models and Algorithms[J]. Minerva Ginecologica,2002,39(7-8):503-513.
[21] SERRA J. Image Analysis and Mathematical Morphology[M]. Salt Lake City, USA:Academic Press,1982.
[22] GARRIGUES S, ALLARD D, BARET F, et al. Quantifying Spatial Heterogeneity at The Landscape Scale Using Variogram Models[J]. Remote Sensing of Environment, 2006, 103(1):81-96.
[23] SHANNON C E. Communication in The Presence of Noise[J]. Institute of Radio Engineers,1949, 37(1):10-21.
[2] KIM J, GRUNWALD S, OSBORNE T Z, et al. Spatial Resolution Effects of Remote Sensing Images on Digital Soil Models in Aquatic Ecosystems[C]. Digital Soil Assessments and Beyond,2012.sydney,Australia.
[3] SANKEY J B, DRAUT A E. Gully Annealing by Aeolian Sediment: Field and Remote-Sensing Investigation of Aeolian-Hillslope-Fluvial Interactions, Colorado River corridor, Arizona, USA[J]. Geomorphology, 2014, 220(3):68-80.
[4] 张俊,王宝山,吕宝庆.面向对象高分辨率影像信息提取中的尺度效应研究[J].北京测绘,2010(1):11-14.
[5] 付杰,张豫.遥感数据尺度特征的研究进展[J].北京测绘,2017(1):32-34.
[6] WOODCOCK C E, STRAHLER A H, JUPP D L B. The Use of Variograms in Remote Sensing: I. Scene Models and Simulated Images[J]. Remote Sensing of Environment,1988,25(3):323-348.
[7] RAHMAN A F, GAMON J A, SIMS D A, et al. Optimum Pixel Size for Hyperspectral Studies of Ecosystem Dunction in Southern California Chaparral and Grassland[J]. Remote Sensing of Environment, 2003, 84(2):192-207.
[8] 韩鹏,龚健雅,李志林.基于信息熵的遥感分类最优空间尺度选择方法[J].武汉大学学报(信息科学版),2008,33(7):676-679.
[9] 柏延臣,王劲峰.基于特征统计可分性的遥感数据专题分类尺度效应分析[J].遥感技术与应用,2004,19(6):443-449.
[10] 刘玉锋,陈冬花,郑朝贵.高分辨率遥感数据云杉林林分冠幅估计[J].新疆农业大学学报,2013,36(2):153-157.
[11] 温兆飞,张树清,白静,等.农田景观空间异质性分析及遥感监测最优尺度选择--以三江平原为例[J].地理学报,2012,67(3):346-356.
[12] 薛冬冬,佘光辉,温小荣,等.基于地统计分析的南京钟山风景区景观格局尺度效应分析[J].西南林业大学学报,2012,32(1):30-35.
[13] ATKINSON P M, CURRAN P J. Choosing An Appropriate Spatial Resolution for Remote Sensing Investigations[J]. Photogrammetric Engineering & Remote Sensing,1997, 63(12):1345-1351.
[14] 张仁铎. 空间变异理论及应用[M]. 北京: 科学出版社, 2005.
[15] TARNAVSKY E, GARRIGUES S, Brown M E. Multiscale Geostatistical Analysis of AVHRR, SPOT-VGT, and MODIS Global NDVI Products[J]. Remote Sensing of Environment, 2008, 112(2):535-549.
[16] JUPP D L B, STRAHLER A H, WOODCOCK C E. Autocorrelation AndRegularization in Digital Images. I. Basic theory[J]. IEEE Transactions on Geoscience & Remote Sensing,1988, 26(4):463-473.
[17] ATKINSON P M, CURRAN P J. Defining AnOptimal Size of Support for Remote Sensing Investigations[J]. IEEE Transactions on Geoscience & Remote Sensing,1995, 33(3):768-776.
[18] JOHNSTON K, HOEF J M V, KRIVORUCHKO K, et al. Using ArcGIS Geostatistical Analyst[M]. Cinderella Manual,Redlands,Califarnia,USA:ESRI,2008.
[19] 王政权. 地统计学及其在生态学中的应用[M]. 北京: 科学出版社,1999.
[20] LANTUéJOUL C. Geostatistical Simulation: Models and Algorithms[J]. Minerva Ginecologica,2002,39(7-8):503-513.
[21] SERRA J. Image Analysis and Mathematical Morphology[M]. Salt Lake City, USA:Academic Press,1982.
[22] GARRIGUES S, ALLARD D, BARET F, et al. Quantifying Spatial Heterogeneity at The Landscape Scale Using Variogram Models[J]. Remote Sensing of Environment, 2006, 103(1):81-96.
[23] SHANNON C E. Communication in The Presence of Noise[J]. Institute of Radio Engineers,1949, 37(1):10-21.