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《Acta Geodaetica et Cartographica Sinica》 2018-06
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A Multi-source DEM Fusion Method Based on Elevation Difference Fitting Neural Network

SHEN Huanfeng;LIU Lu;YUE Linwei;LI Xinghua;ZHANG Liangpei;School of Resource and Environmental Sciences,Wuhan University;Faculty of Information Engineering,China University of Geosciences,Wuhan;School of Remote Sensing and Information Engineering,Wuhan University;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University;  
This paper focuses on machine learning in intelligent photogrammetry:the elevation difference fitting neural network method.The limitations of observation technologies and processing methods lead to the lack of global high-accuracy seamless DEMs,which further restrict DEMs' application in the fields of hydrology,geology,meteorology,military and other applications.Inthispaper,we propose a multi-source DEM fusion method using the neural network model trained based on elevation difference.The proposed method is employed to generate a high-quality seamless DEM dataset blending SRTM1,ASTER GDEM v2,and ICESat GLAS.At first,the ICESat GLAS data were filtered according to the relevant parameters and the elevation differences with DEMs.The threshold of elevation difference adaptively varied with terrain slope to remove the abnormal points effectively.The neural network was then applied to learn the error distribution of ASTER GDEM v2,using the ICESat GLAS data as the control points.We constructed the network input composed of slope information,latitude and longitude coordinates,while the elevation difference of ICESat GLAS and ASTER GDEM v2 were set as the target output.The corrected ASTER GDEM v2 results can be obtained by adding the predicted output to the original elevation values.At last,the corrected ASTER GDEM v2 values were utilized to fill the voids of SRTM1,where the vertical bias between the datasets were dealt with TIN delta surface method to blend the seamless DEM.Randomly selected data were used for actual experiments,and the proposed model was evaluated by comparing with other methods and DEM datasets through quantitative evaluation and visual discrimination.Experiment results show that the proposed method has lower value of RMSE than compared methods both in void or the whole area,which can effectively overcome the influence of outliers in ASTER GDEM v2,and generate seamless DEM.
【Fund】: 国家自然科学基金(61671334;41701394;41661134015)~~
【CateGory Index】: P208;P23
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