Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps via Hierarchical Computation of Median Elevation Regions

TL;DR

This paper introduces a morphological method to generate high-resolution digital elevation models from low-resolution contour maps by hierarchically computing median elevation regions, demonstrating effectiveness with synthetic and real data.

Contribution

It presents a novel hierarchical approach to interpolate elevation contours, leveraging geometric information for high-resolution terrain surface generation from sparse data.

Findings

Effective contour prediction demonstrated on synthetic data

Successful application to Washington, NH contour map

Method is low-cost and robust

Abstract

We proposed a simple yet effective morphological approach to convert a sparse Digital Elevation Model (DEM) to a dense Digital Elevation Model. The conversion is similar to that of the generation of high-resolution DEM from its low-resolution DEM. The approach involves the generation of median contours to achieve the purpose. It is a sequential step of the I) decomposition of the existing sparse Contour map into the maximum possible Threshold Elevation Region (TERs). II) Computing all possible non-negative and non-weighted Median Elevation Region (MER) hierarchically between the successive TER decomposed from a sparse contour map. III) Computing the gradient of all TER, and MER computed from previous steps would yield the predicted intermediate elevation contour at a higher spatial resolution. We presented this approach initially with some self-made synthetic data to show how the contour prediction works and then experimented with the available contour map of Washington, NH to justify its usefulness. This approach considers the geometric information of existing contours and interpolates the elevation contour at a new spatial region of a topographic surface until no elevation contours are necessary to generate. This novel approach is also very low-cost and robust as it uses elevation contours.