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The Digital Terrain Model (DTM) is an important dataset for various applications such as hydrology, environmental sciences, urban planning, etc. Majority of the satellite-based medium resolution (30 m − 90 m) Digital Surface Model (DSM) products are freely available and widely used around the world. The generation of a DTM from these products is a tedious process. In this paper, a simple algorithm is developed to classify the ground and non-ground pixels in the DSM. The algorithm is tested on ALOS – World 3 D 30 m (AW3D30) DSM for small patches in the cities of Chennai, Mumbai and Cairo. Based on general assumption of the minimum elevation difference that exists between the ground and non-ground objects a slope threshold is used to initially classify the DSM. The elevations of the classified pixels are then used iteratively to assign counter values to the pixels. These counter values are finally used to classify the data and the ground pixels are interpolated using Inverse Distance Weighted (IDW) technique to generate the DTM. The proposed algorithm is quantitatively assessed and has a Type I error of 2.33%, Type II error of 1.67%, Total error of 2% and Kappa coefficient value of 96%. Additionally, R 2  and RMSE values of 0.9589 and ± 0.429 m were observed when compared against a reference DTM.

A localized counter-based filtering approach for generating digital terrain model from medium resolution digital surface model