Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM)

Abstract

AbstractThe purpose of the present study is preparing a landform classification by using digital elevation model (DEM) which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER)) and a 90m (the Shuttle Radar Topography Mission; (SRTM)). In the attraction model, scale factors of (S=2, S=3, and S=4) with two neighboring methods of touching (T=1) and quadrant (T=2) are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S=2) gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE) and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of increasing the spatial resolution of DEM (the new DEM with high spatial resolution). In the next step, in order to prepare the geomorphology map using topographic position index (TPI), the DEM with scale factor of (S=2) was used, touching neighborhood serves as input. The landform classes were extracted by using TPI with the new DEM; consequently, the attraction model extraction showed details of landforms that make them more separable than the landform map prepared by utilizing the 90m spatial resolution DEM. Moreover, the results showed that the landform of the 90m spatial resolution DEM (S=2, T=2) and ASTER DEM 30m were similar to each other, these results indicate a high accuracy of the proposed attraction model