An approach to extracting digital elevation model for undulating and hilly terrain using de-noised stereo images of Cartosat-1 sensor

A digital elevation model (DEM) is established as an essential geospatial dataset requisite for many topographical and environmental applications. The freely available DEMs have low spatial resolution (SR >= 30 m) and comprise considerable vertical errors. The vertical errors are worsened in the undulating and hilly or rugged terrain regions. In this research, we introduced a study to investigate the effect of the noise reduction filters on the accuracy and quality of the DEMs for undulating and hilly terrain regions. The main objectives are to extract a high-quality DEM without collecting physical data like ground control points. DEM generation using de-noised stereo images is carried out using Rational Polynomial Coefficients of Cartosat-1 sensor and Automated Tie Point (ATP) selection. The ATP selection and distribution on the stereo images play a significant role in the DEM accuracy. The present paper also provides information about the optimum number of ATPs used for different topographic conditions. The altitude value of extracted DEM through de-noised stereo images and freely accessible DEMs is compared with reference to the ground truth value of the study region. The 3-D surface profile map of the DEM is used for visual interpretation.

Automated summary

This research investigates the impact of noise reduction filters on the accuracy and quality of DEMs in undulating and hilly terrain regions, aiming to extract high-quality DEMs without the need for physical data collection. Using de-noised stereo images, the study compares altitude values with ground truth values and provides information on the optimum number of ATPs for different topographic conditions.