DSMs generation from optical and SAR satellite imagery:
an original unified approach
Prof. Mattia Crespi, Dr. Andrea Nascetti
One of the most important application of remote sensing is the generation of Digital Surface Models (DSMs), that have a large relevance in many engineering, environmental, geosciences, safety and security applications.
Several approaches for DSMs generation exist; here two stereo methodologies (photogrammetry for optical imagery and radargrammetry for SAR imagery) are considered in the frame of an efficient unified approach, both effective for optical and SAR imagery, and based on an original generation of the epipolar imagery (GrEI - Ground quasi-Epipolar Images) and on the semi-global matching (SGM).
This unified approach has been implemented in an automatic and open source software named Digital Automatic Terrain Extractor (DATE), which has been validated on a test site located in Northern Italy, comparing the DSMs extracted to a more accurate reference DSM obtained with LiDAR technology.
The series of lectures will present both the theoretical fundamental of the original unified approach and some practical applications. The software DATE will be left for free at disposal of the summer school attendees.
(4 hours)
Lecture 1: Satellite remote sensing state-of-the-art and DSMs generation approaches
Lecture 2: Original procedure for DSM generation
Lecture 3: DSM quality assessment and procedure evaluation
(4 hours)
Lecture 4: Lab practice: DSM generation from optical imagery with DATE
Lecture 5: Lab practice: DSM generation from SAR imagery with DATE
Lecture 6: Group discussion
Reading list:
[1] G. Agugiaro, D. Poli, F. Remondino. Testfield Trento: Geometric Evaluation of Very High Resolution Satellite Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(B1):191-196, 2012.
[2] Z.G. Bafghi, J. Tian, P. d’Angelo, P. Reinartz. A new algorithm for void filling in a DSM from stereo satellite images in urban areas. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(1):55-61, 2016.
[3] S. Birchfield, C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4):401-406, 1998.
[4] P. Capaldo, M. Crespi, F. Fratarcangeli, A. Nascetti, F. Pieralice. High-resolution SAR radargrammetry: a first application with COSMO- SkyMed SpotLight imagery. IEEE Geoscience and Remote Sensing Letters, 8(6):1100-1104, 2011.
[5] P. Capaldo, M. Crespi, F. Fratarcangeli, A. Nascetti, F. Pieralice. A radargrammetric orientation model and a RPCs generation tool for COSMO-SkyMed and TerraSAR-X high resolution SAR. Italian Journal of Remote Sensing, 44(1):55-67, 2012.
[6] P. Capaldo, M. Crespi, F. Fratarcangeli, A. Nascetti, F. Pieralice, G. Agugiaro, D. Poli, F. Remondino. DSM generation from optical and SAR high resolution satellite imagery: Methodology, problems and potentialities. International Geoscience and Remote Sensing Symposium, pp. 6936-6939, 2012.
[7] P. Capaldo, F. Fratarcangeli, A. Nascetti, F. Pieralice, M. Porfiri, M. Crespi. High resolution radargrammetry - 3D terrain modeling. Land Applications of Radar Remote Sensing, InTech, pp.167-190, 2014.
[8] M. Crespi, F. Fratarcangeli, F. Giannone, F. Pieralice. High resolution satellite image orientation models. Geospatial Technology for Earth Observation, Springer US, pp. 63-104, 2009.
[9] M. Crespi, F. Fratarcangeli, F. Giannone, F. Pieralice. A new rigorous model for high-resolution satellite imagery orientation: application to EROS A and QuickBird. International Journal of Remote Sensing, 33(8):2321-2354, 2012.
[10] C. de Franchis, E. Meinhardt-Llopis, J. Michel, J. Morel, G. Facciolo. An automatic and modular stereo pipeline for pushbroom images. The International Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(3):49-56, 2014.
[11] G. Dial, J. Grodecki. Block adjustment with rational polynomial camera models. Proceedings of APSRS 2002 Annual Conference, Washington DC, pp.9, 2002.
[12] M. Di Rita. Photogrammetric image processing: DSM generation tool for OSSIM. https://trac.osgeo.org/ossim/wiki/GsocDSMGenerationToolForOSSIM, GSoC weekly reports, 2014.
[13] M. Di Rita, A. Nascetti, F. Fratarcangeli, M. Crespi. Upgrade of FOSS DATE plug-in: implementation of a new radargrammetric DSM generation capability. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41(B7):821-825, 2016.
[14] M. Di Rita, A. Nascetti, M. Crespi. Open source tool for DSMs generation from high resolution optical satellite imagery: development and testing of an OSSIM plug-in. International Journal of Remote Sensing, 2017, doi: 10.1080/01431161.2017.
[15] M. Di Rita, A. Nascetti, M. Crespi. FOSS4G DATE for DSMs generation from tri-stereo optical satellite images: development and first results. European Journal of Remote Sensing (in press).
[16] C. S. Fraser, H. B. Hanley. Bias-compensated RPCs for sensor orientation of high-resolution satellite imagery. Photogrammetric Engineering Remote Sensing, 71(8):909-915, 2005.
[17] F. Fratarcangeli, G. Murchio, M. Di Rita, A. Nascetti, P. Capaldo. Digital surface models from ZiYuan-3 triplet: performance evaluation and accuracy assessment. International Journal of Remote Sensing, 37(15):3505-3531, 2016.
[18] M. Gelautz, P. Paillou, C. W. Chen and H. A. Zebker. A comparative study of radar stereo and interferometry for DEM generation. Proceedings of the Fringe workshop, ESA SP-550, 2004.
[19] K. Gutjahr, R. Perko, H. Raggam, M. Schardt. The epipolarity constraint in stereo-radargrammetric DEM generation. IEEE Transactions on Geoscience and Remote Sensing, 52(8):5014-5021, 2014.
[20] H.B. Hanley, C.S. Fraser. Sensor orientation for high-resolution satellite imagery: further insight into bias-compensated RPC. Photogrammetric Engineering and Remote Sensing, 71(8), 2004.
[21] H. Hirschmuller. Stereo Processing by Semi-Global Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):328-341, 2008.
[22] T. Kim. A study on the epipolarity of linear pushbroom images. Photogrammetric Engineering & Remote Sensing, 66(8):961-966, 2000.
[23] T. Krauss, P. Reinartz, M. Lehner, M. Schroeder, U. Stilla. DEM gener- ation from very high resolution stereo satellite data in urban areas using dynamic programming. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(1/W3), 2005.
[24] F.W. Leberl. Radargrammetric image processing. Artech House, Norwood, USA, 1990.
[25] A. Nascetti. High resolution radargrammetry: development and implementation of an innovative image matching strategy, 2013. Doctoral dissertation Advisor: Crespi M.
[26] P.D. Noerdlinger. Atmospheric refraction effects in Earth remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 54(5- 6):360-373, 1999.
[27] M. Pierrot-Deseilligny, I. Clery. APERO, an Open Source Bundle Adjusment Software for Automatic Calibration and Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5/W 16), 2011.
[28] D. Poli. Modelling of spaceborne linear array sensors, 2005. Doctoral dissertation, Swiss Federal Institute of Technology Zurich, Switzerland.
[29] H. Raggam. Surface mapping using image triplets: case studies and benefit assessment in comparison to stereo image processing. Photogrammetric Engineering & Remote Sensing, 72(5):551-563, 2006.
[30] T. Toutin. State-of-the-art of elevation extraction from satellite SAR data. ISPRS Journal of Photogrammetry & Remote Sensing, 55:13-33, 2000.
[31] T. Toutin. Geometric processing of remote sensing images: Models, algorithms and methods. International Journal of Remote Sensing, 25(10):1893-1924, 2004.
[32] M. Wang, F. Hu, J. Li. Epipolar arrangement of satellite imagery by projection trajectory simplification. The Photogrammetric Record, 25(132):422-436, 2010.
[33] M. Wang, F. Hu, J. Li. Epipolar resampling of linear pushbroom satellite imagery by a new epipolarity model. ISPRS Journal of Photogrammetry and Remote Sensing, 66:347-355, 2011.
