Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models

Abayowa, Bernard; Yilmaz, Alper; Hardie, Russell C.

doi:10.1016/j.isprsjprs.2015.05.006

Cited by 57 publications

(25 citation statements)

References 36 publications

(33 reference statements)

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“…2) Uncertainty, imprecision and incompleteness: Distortions in the information extracted from optical images can be caused by radiometric errors like sensor sensibility, illumination changes, atmospheric effects, and geometric errors such as relief displacement, occlusions or shadows [22]. On the other hand, points may be missing in the LiDAR data due to occlusion or presence of water [23].…”

Section: B Challengesmentioning

confidence: 99%

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

Nguyen¹,

Daniel²,

Guériot³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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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 between these datasets may fail to fully profit from 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 or satellite optical imagery with airborne LiDAR data acquired in such context. Firstly, a coarse registration involves processes of extraction and matching of building candidates 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 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 the aforementioned difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by almost a half (i.e. 48.15%) after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the mentioned super-resolution approach. 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 checkpair-line assessment. Promising results yielded by this registration enable further research for a complete versatile fusion methodology between airborne LiDAR and optical imagery data in this challenging context.

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Section: B Challengesmentioning

confidence: 99%

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

Nguyen¹,

Daniel²,

Guériot³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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“…Approaches for reconstructing 3D city models can be categorized as follows: (1) aerial‐image‐based techniques (Frère, Vandekerckhove, Moons, & Goo, ; Früh & Zakhor, ; Grün et al, ; Moons, Frère, Vandekerckhove, & Van Gool, ); (2) ground‐ or airborne‐based laser sensors (Poullis & You, ; Rottensteiner, ; Sun & Salvaggio, ); and (3) combinations (i.e. hybrid) of aerial‐image‐based and ground‐based techniques (Abayowa, Yilmaz, & Hardie, ; Debevec et al, ; Ding, Lyngbaek, & Zakhor, ; Lafarge & Mallet, ; Mastin, Kepner, & Fisher, ; Rottensteiner, ; Turlapaty, Gokaraju, Du, Younan, & Aanstoos, ; Wang & Neumann, ; Yoo, ; Yong & Huayi, ). Recently, the hybrid approach has become popular because terrestrial laser scanners can improve texture quality and produce detailed structural and optical information (Kremer & Hunter, ), whereas aerial images provide texture images that are used to create photorealistic images.…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al () performed similar research to detect or extract 3D buildings from oblique images using mathematical approaches. Recently, Abayowa et al () combined LiDAR and optical imagery to develop automated 3D city models. The research described above was primarily focused on developing methods to automatically construct 3D buildings (i.e.…”

Section: Introductionmentioning

confidence: 99%

Developing an optimized texture mapping for photorealistic 3D buildings

Lee

Yang

2018

Transactions in GIS

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Texture mapping generates photorealistic representations of three-dimensional (3D) geometric objects and enhances the spatial perception of areas of interest. Over the past two decades, even though various approaches for 3D urban models have been investigated, their use has been limited because of the lack of spatial accuracy, details, and the complex processes. It is difficult to maintain highly detailed texture information without using a hybrid of aerial image and ground-based imaging techniques, which are costly. Furthermore, it is hard to develop a fully automated process for 3D urban mapping that achieves high spatial accuracy. With regard to the issues, this research aims to develop a semi-automated process for 3D building models that would help image-based approaches. It helps acquire qualified texture information and improve the appearance of building façades in a large city. In particular, this research first investigates an optimal overlap of consecutive aerial images that generates sufficient information to texture each façade, thus making this process more cost-effective. Second, this research develops an application to semi-automatically build 3D buildings and textured 3D buildings. The application is developed in C++. The textured 3D building models are quantitatively and qualitatively assessed to determine the usability of the semi-automated process.

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“…In aerial imaging, for instance, image and LiDAR data [1] are registered by algorithms exploiting visual cues [2], information theory [3], [4], or surface reconstruction [5], among others. Developments have also been presented in the medical field [6].…”

Section: Introductionmentioning

confidence: 99%

Registration of images to unorganized 3D point clouds using contour cues

Pujol-Miro

Ruiz-Hidalgo

Casas

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Low resolution commercial 3D sensors contribute to computer vision tasks even better when the analysis is carried out in a combination with higher resolution image data. This requires registration of 2D images to unorganized 3D point clouds. In this paper we present a framework for 2D-3D data fusion to obtain directly the camera pose of a 2D color image in relation to a 3D point cloud. It includes a novel multiscale intensity feature detection algorithm and a modified ICP procedure based on point-to-line distances. The framework is generic for several data types (such as CAD designs or LiDAR data without photometric information), and results show that performance is comparable to the state of the art, while avoiding manual markers or specific patterns on the data.

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Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models

Cited by 57 publications

References 36 publications

Robust Building-Based 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

Developing an optimized texture mapping for photorealistic 3D buildings

Registration of images to unorganized 3D point clouds using contour cues

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