Lunar Surface Reconstruction from Apollo MC Images

Elaksher, Ahmed; Al-Jarrah, Ahmad; Walker, Kyle E.

doi:10.1007/s11038-015-9468-8

Cited by 1 publication

(1 citation statement)

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“…With new methods or modification to existing ones, researchers are still engaged with feature extraction and matching, especially for photogrammetric applications, with SIFT-like algorithms dominating the literature [ 35 ]. The SIFT algorithm or variants are successfully used in various DEM extraction applications, among which recent and captivating initiatives of topography extraction from Synthetic Aperture Radar (SAR) images [ 36 ], high accuracy lunar surface reconstruction from the Apollo 16 Mapping Camera (MC) [ 37 ], and archeological site topographic modeling from a compact camera onboard an Unmanned Aerial Vehicle (UAV) [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

Daliakopoulos

Tsanis

2019

Sensors

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A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements.

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