Automatic Radiometric Improvement of Moon Images for Shadow Segmentation
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Kaufmann et al.  have proposed a method for pose estimation of the spacecraft during the descent phase by matching the shadows between real time images and pre-rendered reference images. The shadow segmentation of the real time images is largely affected by the Lunar surface reflectance, the Lunar surface topography, the Lunar surface features and the illumination conditions. This thesis investigates various radiometric enhancement methods to reduce the effect of these artefacts on the shadow segmentation. An enhancement pipeline was designed to enhance the contrast of the images. An automated classification of images was also implemented in the pipeline based on topographical information and mathematical parameters. Images taken with the Narrow Angle Camera (NAC) during the Lunar Reconnaissance Orbiter (LRO) mission were used to develop the automated classification logic of the pipeline and to validate the performance of the pipeline. The reference images were rendered based on the Digital Terrain Model (DTM) files of the corresponding NAC images.The result shows that enhancement of the descent images increases the amount of segmented shadows, when compared with the shadow segmented original image. The percentage of correct shadow match between the shadow segmented virtual image and shadow segmented enhanced images are higher compared to the shadow segmented original image. Further, it is observed that the applied enhancement method depends on the surface reflectance and the incidence angle.
Place, publisher, year, edition, pages
2015. , 84 p.
Technology, Image processing, Optical navigation, Shadow segmentation, Radiometric enhancement, Lunar image classification
IdentifiersURN: urn:nbn:se:ltu:diva-45668Local ID: 3580b5ca-2dc5-4929-b518-c9dcd555a01eOAI: oai:DiVA.org:ltu-45668DiVA: diva2:1018965
Subject / course
Student thesis, at least 30 credits
Space Engineering, master's level
Validerat; 20150923 (global_studentproject_submitter)2016-10-042016-10-04Bibliographically approved