An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models  

作　　者：Hessah ALBANWAN Rongjun QIN 

机构地区：[1]Department of Civil Engineering,Kuwait University,Kuwait 72504,Kuwait [2]Geospatial Data Analytics Lab,The Ohio State University,Columbus 43210,USA [3]Department of Civil,Environmental and Geodetic Engineering,The Ohio State University,Columbus 43210,USA [4]Department of Electrical and Computer Engineering,The Ohio State University,Columbus 43210,USA [5]Translational Data Analytics Institute,The Ohio State University,Columbus 43210,USA

出　　处：《Journal of Geodesy and Geoinformation Science》2022年第4期1-9,共9页测绘学报（英文版）

基　　金：John Hopkins University Applied Physics Lab to support the Imagery of the 2019 DFC datasets

摘　　要：The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

关 键 词：Digital Surface Model(DSM) DSM Fusion adaptive fusion satellite stereo images 

分 类 号：P228[天文地球—大地测量学与测量工程]