Coarse-to-fine registration of airborne LiDAR data and optical imagery on urban scenes

Authors: Nguyen, Thanh Huy; Daniel, Sylvie; Guériot, Didier; Sintès, Christophe; Le Caillec, Jean-Marc
Abstract: Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration of these datasets fails to fully profit from the potential of both sensors. An optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when data are collected at different times, from different platforms, under different acquisition configurations. This article presents a coarse-to-fine registration method of optical imagery with airborne LiDAR data acquired in such context. First, a coarse registration involves processes of extraction and matching of building candidates from the two datasets. Then, a mutual-information-based fine registration is carried out. It involves a superresolution approach applied to LiDAR data to generate images with the same resolution as the optical image, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with this difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48.15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the superresolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a check-point assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further researches for a complete fusion methodology between these datasets in this challenging context.
Document Type: Article de recherche
Issue Date: 28 May 2020
Open Access Date: 11 November 2020
Document version: VoR
Creative Commons Licence: https://creativecommons.org/licenses/by/4.0
Permalink: http://hdl.handle.net/20.500.11794/67174
This document was published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, 3125-3144
https://doi.org/10.1109/JSTARS.2020.2987305
IEEE
Alternative version: 10.1109/JSTARS.2020.2987305
Collection:Articles publiés dans des revues avec comité de lecture

Files in this item:
Description SizeFormat 
JSTARS2987305.pdf10.08 MBAdobe PDFThumbnail
View/Open
All documents in CorpusUL are protected by Copyright Act of Canada.