The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion

Daniel, Sylvie

Gueriot, Didier

Le Caillec, Jean-Marc

Nguyen, Thanh Huy

Sintès, Christophe

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

English

arXiv

Crossref

Robust Building-Based Registration of Airborne Lidar Data and Optical Imagery on Urban Scenes

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

Guériot, Didier

CorpusUL

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

Robust Building-based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

International audienceThe motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration , the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion

Sintes, Christophe

HAL-Université de Bretagne Occidentale

Robust Building-Based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

International audienceApplications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration between these datasets may fail to fully profit the potential of both sensors. In this regard, 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 considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper presents a coarse-to-fine registration method of aerial/satellite optical imagery with airborne LiDAR data acquired in such context. Firstly, a coarse registration involves extracting and matching of buildings from LiDAR data and optical imagery. Then, a Mutual Information-based fine registration is carried out. It involves a super-resolution approach applied to LiDAR data, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with the aforementioned difficult context. 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 addressed by the mentioned super-resolution. 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 research for a complete versatile fusion methodology between airborne LiDAR and optical imagery data in this challenging context

Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

DANIEL, Sylvie

GUERIOT, Didier

SINTES, Christophe

Archive Ouverte en Sciences de l'Information et de la Communication

Robust Building-based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

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CorpusUL

CorpusUL

HAL-Université de Bretagne Occidentale

HAL-Université de Bretagne Occidentale

Archive Ouverte en Sciences de l'Information et de la Communication