To extract the high-quality DEM in complicated mountain areas,a DEM fusion method for ascending and descending orbit StereoSAR DEMs considering Synthetic Aperture Radar(SAR)echo intensity is proposed.After the analysi...To extract the high-quality DEM in complicated mountain areas,a DEM fusion method for ascending and descending orbit StereoSAR DEMs considering Synthetic Aperture Radar(SAR)echo intensity is proposed.After the analysis for the influence of terrain features and SAR side-looking imaging characteristics on radar echo intensity and DEM accuracy,four Terras AR-X images with the stripmap mode and the 3 m spatial resolution covering a certain area of Maoxian County,Sichuan Province,China,was selected as the experimental area.StereoSAR technology was used to extract the ascending orbit StereoSAR DEM and the descending orbit StereoSAR DEM,respectively,and the corresponding radar echo intensity map was calculated.Then,while comparing the radar echo intensity corresponding to the same point position,DEM fusion was carried out,and the accuracy of DEM before and after the fusion was analyzed with the ground points measured by GNSS-RTK as reference data.Finally,a high-quality DEM with a 3 m spatial resolution in the experimental area was obtained.The DEM accuracy was improved on all slopes,and the mean absolute deviation(MAD)improved to 4.798 m,the standard deviation(SD)improved to 6.087 m and the LE90 improved to 40.48 m.The experimental results indicate that the fusion method of highresolution ascending and descending orbit StereoSAR DEMs considering SAR echo intensity can effectively extract DEM with high accuracy and reliability,which can provide technical support for obtaining highquality terrain information in similar areas.展开更多
Digital elevation models(DEMs)are a necessary dataset for modelling the Earth’s surface;however,all DEMs contain error.Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be availabl...Digital elevation models(DEMs)are a necessary dataset for modelling the Earth’s surface;however,all DEMs contain error.Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be available for a region.However,the use of a clustering algorithm in DEM fusion has not been previously reported.In this study a new DEM fusion algorithm based on a clustering approach that works on multiple DEMs to exploit consistency in the estimates as indicators of accuracy and precision is presented.The fusion approach includes slope and elevation thresholding,k-means clustering of the elevation estimates at each cell location,as well as filtering and smoothing of the fusion product.Corroboration of the input DEMs,and the products of each step of the fusion algorithm,with a higher accuracy reference DEM enabled a detailed analysis of the effectiveness of the DEM fusion algorithm.The main findings of the research were:the k-means clustering of the elevations reduced the precision which also impacted the overall accuracy of the estimates;the number of final cluster members and the standard deviation of elevations before clustering both had a strong relationship to the error in the k-means estimates.展开更多
基金supported by Study on Early Identification of Landslide Hazards based on Highresolution SAR Image[KJ-2018-13]。
文摘To extract the high-quality DEM in complicated mountain areas,a DEM fusion method for ascending and descending orbit StereoSAR DEMs considering Synthetic Aperture Radar(SAR)echo intensity is proposed.After the analysis for the influence of terrain features and SAR side-looking imaging characteristics on radar echo intensity and DEM accuracy,four Terras AR-X images with the stripmap mode and the 3 m spatial resolution covering a certain area of Maoxian County,Sichuan Province,China,was selected as the experimental area.StereoSAR technology was used to extract the ascending orbit StereoSAR DEM and the descending orbit StereoSAR DEM,respectively,and the corresponding radar echo intensity map was calculated.Then,while comparing the radar echo intensity corresponding to the same point position,DEM fusion was carried out,and the accuracy of DEM before and after the fusion was analyzed with the ground points measured by GNSS-RTK as reference data.Finally,a high-quality DEM with a 3 m spatial resolution in the experimental area was obtained.The DEM accuracy was improved on all slopes,and the mean absolute deviation(MAD)improved to 4.798 m,the standard deviation(SD)improved to 6.087 m and the LE90 improved to 40.48 m.The experimental results indicate that the fusion method of highresolution ascending and descending orbit StereoSAR DEMs considering SAR echo intensity can effectively extract DEM with high accuracy and reliability,which can provide technical support for obtaining highquality terrain information in similar areas.
基金Canadian Space Agency:[Grant Number New Directions Grant]Ontario Ministry of Agriculture,Food and Rural Affairs+1 种基金GEOmatics for Informed DEcisionsOntario Research Fund.The authors would like to acknowledge the Canadian Space Agency,GEOIDE,Ontario Research Fund,and OMAFRA for providing research funding.
文摘Digital elevation models(DEMs)are a necessary dataset for modelling the Earth’s surface;however,all DEMs contain error.Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be available for a region.However,the use of a clustering algorithm in DEM fusion has not been previously reported.In this study a new DEM fusion algorithm based on a clustering approach that works on multiple DEMs to exploit consistency in the estimates as indicators of accuracy and precision is presented.The fusion approach includes slope and elevation thresholding,k-means clustering of the elevation estimates at each cell location,as well as filtering and smoothing of the fusion product.Corroboration of the input DEMs,and the products of each step of the fusion algorithm,with a higher accuracy reference DEM enabled a detailed analysis of the effectiveness of the DEM fusion algorithm.The main findings of the research were:the k-means clustering of the elevations reduced the precision which also impacted the overall accuracy of the estimates;the number of final cluster members and the standard deviation of elevations before clustering both had a strong relationship to the error in the k-means estimates.
