Abstract
In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
Highlights
Digital Surface Model (DSM) performs an important role in various applications such as: planning; 3D urban city maps; natural disaster management; civilian emergencies; military activities; airport management; and, geographical analysis, such as crime and hazards (Saeedi and Zwick 2008)
Two pairs of satellite images have been used in this research, first the DSMs have been produced, and later they have been merged using both Maximum Likelihood and Bayesian approaches
It is assumed the errors are random and they can be represented by a Gaussian distribution with mean equal to elevation of the used DSM and variance numerically linked to the uncertainty in the digital surface model, measured by evaluating the quality, using checkpoints, and blunder is detected and eliminated
Summary
DSMs performs an important role in various applications such as: planning; 3D urban city maps; natural disaster management (e.g. flooding, earthquake, landslide); civilian emergencies; military activities; airport management; and, geographical analysis, such as crime and hazards (Saeedi and Zwick 2008). Different terms have been used to indicate the merging such as fusion, combination, integration and synergy. These all can be considered to be a synonym for merging (Papasaika-Hanusch, 2012). Data merging has been studied by different researchers. It can be used, potentially, to identify the highest quality data for an area, as well as to address problems of data volume. Papasaika et al (2009) used merging to solve the problem of poor image matching technique by incorporating extra data sources. Two pairs of satellite images have been used in this research, first the DSMs have been produced, and later they have been merged using both Maximum Likelihood and Bayesian approaches.
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