Paper
22 October 2004 DTMs generation from satellite stereo images: accuracy tests in mountain region
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Abstract
Digital Terrain Models (DTM) represent an effective tool for many applications and in particular for terrain morphology investigation and orthoimages generation. The availability of satellite stereo images allows to generate updated DTMs through digital photogrammetric algorithms especially in those areas where old and poor maps exist. In this work we show some quality tests results about DEMs obtained from ASTER data (15m geometric resolution), elaborated through commercial software. We consider this information very important to understand which kind of applications can reasonably use these data. Shown results refer to a mountain area located in the NW Italian Alps, characterized by different height type regions (flat, hilly, mountain) whose evaluation can drive to consistent results for a better understanding of limits and forces of these data. Such tests, making use of the commercial software AsterDTM, take into consideration both qualitative and quantitative aspects. Height profiles comparisons, statistical analysis on differences and data mining have been carried out in order to evaluate accuracies and to define the nature of possible systematic errors. Reference data is the available Regional DTM (50m x 50m grid, accuracy of 2.5m) and the single height points extracted from technical maps at 1:10000 scale for more precise local investigation.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Enrico Borgogno Mondino, F. Giulio Tonolo, P. Boccardo, and T. Bellone "DTMs generation from satellite stereo images: accuracy tests in mountain region", Proc. SPIE 5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, (22 October 2004); https://doi.org/10.1117/12.565564
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Cited by 4 scholarly publications.
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KEYWORDS
Satellites

Satellite imaging

Earth observing sensors

Statistical analysis

Clouds

Error analysis

Data mining

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