Use of noise reduction filters on stereo images for improving the accuracy and quality of the digital elevation model

Recently, there has been increasing attention to the digital elevation model (DEM) because of its ability to learn the Earth's surface's topography. Freely accessible DEMs such as the Cartosat-1, shuttle radar topography mission (SRTM), light detection and ranging, and advanced spaceborne thermal emission and reflection radiometer DEM, contain large vertical errors. The errors are aggravated over multifaceted geography and cannot rectify microgeographic deviations in the moderately flat landscape. As high-accuracy DEMs have limited availability, dated low-accuracy DEMs are still used in various models, specifically in the data-sparse areas. However, it is necessary to enhance the quality of these DEMs before their use in the geomorphometric analysis. We aim to investigate the effect of noise reduction filters on DEM's accuracy and quality. The noise reduction filters such as weighted average filter, median filter, sharpen filter, Lee sigma filter, and local sigma filter are used for noise reduction in the stereo images. The quality and accuracy of the generated DEMs are further improved by selecting an optimum number of tie points in the image matching process. The effects are assessed by correlating the surface profiles for the final DEM obtained and SRTM DEM as a reference with a resolution of 1 arc sec. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

This research focuses on investigating the impact of noise reduction filters on the accuracy and quality of DEMs. By using filters such as weighted average filter, median filter, sharpen filter, Lee sigma filter, and local sigma filter in stereo images for noise reduction, the quality and accuracy of generated DEMs are enhanced. The effects are evaluated by selecting an optimal number of tie points in the image matching process.