3D stereo reconstruction:  high resolution satellite video

M. Chernetskiy; Y. Tao; J-P. Muller

doi:10.1117/12.2533226

7 October 2019 3D stereo reconstruction: high resolution satellite video

M. Chernetskiy, Y. Tao, J-P. Muller

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 111551V (2019) https://doi.org/10.1117/12.2533226
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Precise high-resolution Digital Elevation Models (DEMs) are essential for creation of terrain relief and associated terrain hazard area maps, urban land development, smart cities and in other applications. The 3D modelling system entitled the UCL Co-registration Ames Stereo Pipeline (ASP) Gotcha Optimised (CASP-GO) was demonstrated on stereo data of Mars to generate 3D models for around 20% of Martian surface using cloud computers which was reported in 2018. CASP-GO is an automated DEM/DTM processing chain for NASA Mars, lunar and Earth Observation data including Mars 6m Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) 25cm stereo-data as well as ASTER 18m stereo data acquired on the NASA EOS Terra platform. CASP-GO uses tie-point based multi- resolution image co-registration, combined with sub-pixel refinement and densification. It is based on a combination of the NASA ASP and an adaptive least squares cor- relation and region growing matcher called Gotcha (Gruen-Otto-Chau). CASP-GO was successfully applied to produce more than 5300 DTMs of Mars (http://www.i- Mars.eu/web-GIS). This work employs CASP-GO to obtain DEMs from high resolution Earth Observation (EO) satellite video system SSTL Carbonite-2. CASP- GO was modified to work with multi-view point-and-stare video data including subpixel fusion of point clouds. Multi-view stereo video data are distinguished from still image data by a richer amount of information and noisier water areas.

Citation Download Citation

M. Chernetskiy, Y. Tao, and J-P. Muller "3D stereo reconstruction: high resolution satellite video", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551V (7 October 2019); https://doi.org/10.1117/12.2533226

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