Automatic Generation of Digital Elevation Model Using Geo Eye-1 Stereo-pair Imagery

Sowmya D R^1, , P Deepa Shenoy¹ and Venugopal K R¹

¹Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India

Cite this paper:
Sowmya D R, P Deepa Shenoy and Venugopal K R. Automatic Generation of Digital Elevation Model Using Geo Eye-1 Stereo-pair Imagery. Journal of Geosciences and Geomatics. 2019; 7(5):212-220. doi: 10.12691/jgg-7-5-1

Abstract

In this paper, we have generated a 5m Digital Elevation Model (DEM) from GeoEye-1 along-track stero pair satellite imagery. The Rational Polynomial Coefficients (RPCs) provided by vendors are biased using the Rational Function Model (RFM) to improve the geo-positional accuracy. The texture features and edge features are extracted to efficiently identify the conjugate points. Epipolar resampling is performed and Normalized Correlation Coefficient (NCC) is used as a template matching technique. The patch transformation using the slope and aspect analysis is performed to match the conjugate points and whose success rate is more than without using patch transformation. The points which satisfy the defined correlation factor are accepted for DEM construction. The RMSE at five different random points of generated DEM are calculated using Google earth data and found better than LPS eATE algorithm.

Keywords:
Digital Elevation Model (DEM) Epipolar Resampling Features Normalized Correlation Coefficient (NCC) Patch Transformation Rational Polynomial Coefficients (RPCs)

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References:

[1]	Jannati M, Valadan Zoej M, & Mokhtarzade M. “Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery using the Rigorous Sensor Model”, Sensors, Vol.17, no.1, pp.129, 2017
[2]	Du Wen-Liang, “A Fast Dense Feature-Matching Model for Cross-Track Pushbroom Satellite Imagery”, Sensors Vol.18, no.12, pp. 4182, 2018.
[3]	Oh J, & Lee C, “Extraction of Digital Elevation Model using Stereo Matching with Slope-Adaptive Patch Transformation”, KSCE Journal of Civil Engineering, Vol.20, no.7, pp. 2902-2909, 2016.
[4]	Hu F, X. M. Gao, G. Y. Li, and M. Li. “DEM Extraction from Worldview-3 Stereo-Images and Accuracy Evaluation”, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences , Vol.41 ,2016.
[5]	Shaker, Ahmed, Wai Yeung Yan, and Said Easa, “Using Stereo Satellite Imagery for Topographic and Transportation Applications: An Accuracy Assessment”, GIScience & Remote Sensing Vol. 47.no.3, pp: 321-337, 2010.
[6]	Toutin. T, “Radarsat-2 DSM Generation with New Hybrid, Deterministic, and Empirical Geometric Modeling without GCP”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, no. 5 pp: 2049-2055, 2011.
[7]	Papasodoro C, “Potential of RADARSAT-2 Stereo Radargrammetry for the Generation of Glacier DEMs”, Journal of Glaciology, Vol.62, No.233, PP. 486-496, 2016.
[8]	Noh, Myoung-Jong, and Ian M. Howat. “Automated Stereo-Photogrammetric DEM Generation at High Latitudes: Surface Extraction with TIN-based Search-Space Minimization (SETSM) Validation and Demonstration Over Glaciated Regions." GIScience & Remote Sensing Vol.52.No.2, pp: 198-217, 2015.
[9]	Jiao, Niangang, Feng Wang, Hongjian You, Mudan Yang, and Xinghui Yao. "Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery based on Statistical Learning Theory." Sensors Vol.18, no. 6,pp: 1701, 2018.
[10]	Lu. H, Suo. Z, Li. Z, Xie. J, Zhao. J, & Zhang. Q. “InSAR Baseline Estimation for Gaofen-3 Real-Time DEM Generation”, Sensors, Vol.18, No.7, pp. 2152, 2018.
[11]	Chen.T, Shibasaki. R, Tsuno. K, & Morita. K, “Triplet-Matching for DEM Generation with PRISM, ALOS-A Case Study using Air-Borne Three Line Scanner Data” Citeseer, 2004.
[12]	Noh, Myoung-Jong, and Ian M. Howat, “Automatic Relative RPC Image Model Bias Compensation through Hierarchical Image Matching for Improving DEM Quality”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.136, pp. 120-133, 2018.
[13]	Shen. X, Li. Q, Wu. G & Zhu.J, “Bias Compensation for Rational Polynomial Coefficients of High-Resolution Satellite Imagery by Local Polynomial Modelling”, Remote Sensing, Vol.9, No.3, pp. 200, 2017.
[14]	Cho, Woosug, Toni Schenk, and Mustafa Madani. “Resampling Digital Imagery to Epipolar Geometry”, International Archives of Photogrammetry and Remote Sensing, Vol.29, pp. 404-404, 1993.
[15]	Kim, Jae-In, and Taejung Kim, “Comparison of Computer Vision and Photogrammetric approaches for Epipolar Resampling of Image Sequence”, Sensors, Vol.16, no. 3, pp. 412, 2016.
[16]	Koh, Jin-Woo, and Hyun-Seung Yang, “Unified Piecewise Epipolar Resampling Method for Pushbroom Satellite Images”, EURASIP Journal on Image and Video Processing, Vol.2016, no. 1, pp.11, 2016
[17]	Yang, M., Mei, H., Yang, Y., & Huang, D “Efficient Storage Method for Massive Remote Sensing Image via Spark-Based Pyramid Model”, International Journal of Innovative Computing, Information and Control, Vol. 13, no. 6, pp: 1915-1929, 2017.
[18]	Zhou. M, & Zhang. J. L, “Scale Transformation for Remote Sensing Image and Optimal Scale Selection”, 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Atlantis Press, 2013.
[19]	Zhou. G, Luo. Q, Xie. W, Yue. T, Huang. J & Shen. Y, “Transformation Model with Constraints for High-Accuracy of 2D-3D Building Registration in Aerial Imagery”, Remote Sensing, Vol. 8, No.6, pp.507, 2016.
[20]	Sun. Y, Zhao. L, Zhou. G & Yan. L, “Absolute Orientation based on Distance Kernel Functions”, Remote Sensing, Vol.8, No.3, pp: 213, 2016.