Error Detection and DEM Fusion Using Self-Consistency
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
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Nowadays, different sensors and processing techniques provide Digital Elevation Models (DEMs) for the same site, which differ significantly with regard to their geometric characteristics and accuracy. Each DEM contains intrinsic errors due to the primary data acquisition technology, the processing chain, and the characteristics of the terrain. DEM fusion aims at overcoming the limitations of different DEMs by merging them in an intelligent way. In this paper we present a generic algorithmic approach for fusing two arbitrary DEMs, using the framework of sparse representations. We conduct extensive experiments with real DEMs from different earth observation satellites to validate the proposed approach. Our evaluation shows that, together with adequately chosen fusion weights, the proposed algorithm yields consistently better DEMs.