Abstract
Abstract. DATE (Digital Automatic Terrain Extractor) is a Free and Open Source Software for Geospatial (FOSS4G), which combines photogrammetric and computer vision algorithms in order to automatically generate DSMs from multi-view SAR and optical high resolution satellite imagery, following an iterative and pyramidal workflow in order to refine a coarse DSM used as reference. Consequently, DATE is able to face both the issues of DSM generation and epipolar resampling of satellite imagery. The aim of this work is to evaluate DATE performance, by carrying out a sensitivity analysis based on the dense matching parameters. In particular, DATE implements the Semi-Global Block Matching (SGBM) algorithm, a modified version of Semi-Global Matching method: thus, the sensitivity analysis aims at assessing how SGBM parameters – namely, the difference between maximum and minimum disparity (ndisparities), the minimum disparity value (minimumDisp) and the matched block size (SADWindowSize) – affect the efficiency of the disparity map computation and the final DSM accuracy. The analysis focuses on the case study of Trento and of the Adige Valley, which was chosen due to its geomorphological heterogeneity and complexity, allowing to perform an accuracy evaluation on four tiles, characterized by specific roughness frequencies and morphologies (thus having different effects on disparity variations). Several practical indications on the optimal and critical parameter combinations were retrieved; in addition to this, this work highlighted the most influential parameters both in terms of accuracy (minimumDisp) and computation time (ndisparities), paving the way to further principal component analyses. Finally, the obtained results showed no clear relationship between the area morphology and the solution structure.
Highlights
Digital Surface Models (DSMs) play a role of primary importance in several processes, disciplines and research fields, since they are a numeric representation of the elevation of the first reflective surface of the Earth, which can be studied and automatically processed in an efficient way
Several surveying and remote sensing technologies allow for retrieving data useful to DSM generation, such as terrestrial and airborne LiDAR and photogrammetry, SAR and optical satellite imagery
The aim of this work was to evaluate the influence of Semi-Global Matching parameters on DATE performance, on generated DSMs accuracy and on disparity map processing time, for the case study of Trento and of the Adige valley, due to its geomorphological heterogeneity
Summary
Digital Surface Models (DSMs) play a role of primary importance in several processes, disciplines and research fields, since they are a numeric representation of the elevation of the first reflective surface of the Earth, which can be studied and automatically processed in an efficient way. Several surveying and remote sensing technologies allow for retrieving data useful to DSM generation, such as terrestrial and airborne LiDAR and photogrammetry, SAR and optical satellite imagery. Satellite images are characterized (if compared to terrestrial ones) by significant distortions, due to opticalgeometric sensor characteristics and to atmospheric refraction. For this kind of images, the epipolar geometry, which is useful to the photogrammetric process, is quite challenging to achieve; in other words, the epipolar resampling represents a difficult issue to solve (Kim, 2000). Several studies have been carried out in order to define the epipolar model for satellite sensors and how to achieve epipolar image resampling (Koh and Yang, 2016)
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