State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Chen, Ziyue; Gao, Bingbo; Devereux, Bernard

doi:10.3390/s17010150

Cited by 166 publications

(148 citation statements)

References 79 publications

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“…The threshold is set as follows, T e = H g + T rf , where H g denotes the ground elevation and T rf is a relief factor. The first value H g , as proposed by [45], can be determined from a Digital Terrain Model (DTM) generated from the LiDAR point cloud data [46]. Otherwise, since the LiDAR point cloud contains information of the point classification (as described in Table I), we can also measure H g by the average elevation of ground points, i.e.…”

Section: A Coarse Registrationmentioning

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: A Coarse Registrationmentioning

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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“…Many techniques and approaches, being more or less effective, were developed in the recent years. Chen et al (2017) provide a good review of the state of the art methods for the generation of MDT techniques using point clouds generated by Aerial LiDAR Systems (ALS). Despite the extensive review and great progress that has been made, it is recognized by these authors that the generation of DTM remains a challenge, in certain situations.…”

Section: Introductionmentioning

confidence: 99%

AN EFFICIENT METHOD TO CREATE DIGITAL TERRAIN MODELS FROM POINT CLOUDS COLLECTED BY MOBILE LiDAR SYSTEMS

Gézero

Antunes

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate "terrain points" from "no terrain points", quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain's shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

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“…Vehicle recognition from an aerial Lidar point cloud is a fundamental task for many Lidar applications, such as digital elevation model generation (Chen et al 2017, Yang et al 2016, urban parking management (Liu et al 2016), traffic management and smart city modeling (Lafarge et al 2012, Xiao et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Recognition in Aerial Lidar Point Cloud Based on Dynamic Time Warping

Zhang¹,

Vosselman²,

Elberink³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A two-step vehicle recognition method from an aerial Lidar point cloud is proposed in this paper. First, the Lidar point cloud is segmented using the region-growing algorithm with vehicle size limitation. Then the vehicle is recognized according to the profile shape based on dynamic time warping. The proposed method can detect vehicles parking under trees in an urban scene, and classifies the vehicles into different classes. The vehicle location, orientation, parking direction and size can also be determined. The experimental result based on a real urban Lidar point cloud shows that the proposed method can correctly recognize 95.1% of vehicles.

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State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Cited by 166 publications

References 79 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

AN EFFICIENT METHOD TO CREATE DIGITAL TERRAIN MODELS FROM POINT CLOUDS COLLECTED BY MOBILE LiDAR SYSTEMS

Vehicle Recognition in Aerial Lidar Point Cloud Based on Dynamic Time Warping

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