%0 Journal Article %1 rs14081778 %A Wang, Jinghui %A Gong, Ke %A Balz, Timo %A Haala, Norbert %A Soergel, Uwe %A Zhang, Lu %A Liao, Mingsheng %D 2022 %J Remote Sensing %K journal review %N 8 %R 10.3390/rs14081778 %T Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions %U https://www.mdpi.com/2072-4292/14/8/1778 %V 14 %X Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

@article{rs14081778, abstract = {Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.}, added-at = {2022-04-25T08:07:38.000+0200}, article-number = {1778}, author = {Wang, Jinghui and Gong, Ke and Balz, Timo and Haala, Norbert and Soergel, Uwe and Zhang, Lu and Liao, Mingsheng}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29cfc2c685ef39e7a7d1dcf084211a130/markusenglich}, doi = {10.3390/rs14081778}, interhash = {2c3d181cc3621c7571e0397e83d36825}, intrahash = {9cfc2c685ef39e7a7d1dcf084211a130}, issn = {2072-4292}, journal = {Remote Sensing}, keywords = {journal review}, number = 8, timestamp = {2022-04-25T06:07:38.000+0200}, title = {Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions}, url = {https://www.mdpi.com/2072-4292/14/8/1778}, volume = 14, year = 2022 }