Enhancing the Resolution of Digital Elevation Models Using Surface Normal from Planetary Images

Abstract

A digital elevation model (DEM) can be generated using a gradient of a single image and a stereo image pair from an imaging sensor or laser altimeter sensor on an orbiter. These DEMs are affected by intrinsic problems such as stereo noise in the triangulation process or low spatial resolution from sparse measurement points. Various image-based techniques are presently used to enhance the resolution of DEMs, but they can be very challenging since calculating each surface point direction vector from one or more partially overlapped images requires many assumptions. In this paper, an approach is proposed to enhance the resolution of a coarse DEM, using the information of a surface normal extracted from an ortho-rectified planetary image dataset. The surface normal extracted from the ortho-rectified planetary image dataset and coarse DEM are well-aligned, and the surface normal also offers direction vectors of uniform quality at every surface point.The keypoint of this improvement is the separation of the surface normal as a map, to overcome the ambiguity in the role of surface direction information. The coarse DEM is then optimized using the high-resolution surface normal information, so that it displays the characteristics of the terrain in the surface normal map. For performance analysis, the results obtained with the proposed method were compared with a DEM generated using the socet set next generation automatic terrain extraction (NGATE) program of BAE systems. Using the proposed method, the details of the terrain in the high-resolution surface normal image and the output DEM were well estimated.