Registration of aerial imagery and aerial LiDAR data using centroids of plane roof surfaces as control information

Kwak, Tae-Suk; Kim, Yong‐Il; Yu, Ki-Yun; Lee, Byoung Kil

doi:10.1007/bf02830090

Cited by 41 publications

(22 citation statements)

References 2 publications

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“…Line features were used by [12] to co-register both image and elevation data. For extracting building roofs using LiDAR data and multispectral imagery, [13] extracted the roof planes from both optical and LiDAR data and then co-related their centroids. On the other hand, intensity-based registration methods evaluate statistical dependency between the image and elevation datasets in order to derive similarity measures.…”

Section: Previous Workmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

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The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

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Section: Previous Workmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

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“…A method for optical images and LiDAR data registration has been proposed by Kwaket al, [23] which uses centroids of plane roof surfaces as control information. This research was done on scenes where roofs are plane.…”

Section: Building Roofsmentioning

confidence: 99%

A Review of Optical Imagery and Airborne LiDAR Data Registration Methods

Mishra

Zhang²

2012

TORMSJ

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Representing a scene completely from remote sensing data requires both spectral and 3-D-surface information. Integration of spectral information from optical images and 3-D-surface information from LiDAR is important in a number of remote sensing applications such as feature extraction, image classification, image analysis, building extraction, 3-D city modelling, canopy modelling etc. Therefore, numerous methods have been developed in the last decade to align both data sets into a common reference frame to effectively utilize their complementary characteristics. However, due to the significantly different characteristics between optical image and LiDAR data, there are a number of technical challenges in the alignment of both data sets. Different research papers introduced different strategy or methodology to overcome the challenges, reaching different alignment/registration results. This paper presents a review of classical and up to date optical-LiDAR registration methods with the emphasis on control point detection and matching. The aim of this paper is to provide readers with an overview of existing methods, identify their advantages and limitations, and give readers the overall information on what will be useful for researchers and practitioners to realistically select proper method for their application.

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“…Though there is lot of literature describes image-to-image registration (Brown, 1992), both automatically or semi-automatically, however, little literature concerns the problem of registration between LiDAR data and imagery up to date. In some research straight line features (Habib, 2005) and planer patches (Kwak, 2006;Bang, Habib 2008) were used in two separate methodologies as the primitive of choice for the co-registration of the photogrammetric datasets to the LiDAR coordinate system. The approach using straight line features and planer patches starts with generating a photogrammetric model through a photogrammetric triangulation using an arbitrary datum without knowledge of any control information (Habib, 2005).…”

Section: Instructionsmentioning

confidence: 99%

The Direct Registration of Lidar Point Clouds and High Resolution Image Based on Linear Feature by Introducing an Unknown Parameter

Yao

Guang

2012

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

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ABSTRACT:The registration between optical images and point clouds is the first task when the combination of these two datasets is concerned. Due to the discrete nature of the point clouds, and the 2D-3D transformation in particular, a tie points based registration strategy which is commonly adopted in image-to-image registration is hard to be used directly in this scenario. A derived collinear equation describing the map relationship between an image point and a ground point is used as the mathematical model for registration, with the point in the LiDAR space expressed by its parametric form. such a map relation can be viewed as the mathematical model which registers the image pixels to point clouds. This model is not only suitable for a single image registration but also applicable to multiple consecutive images. We also studied scale problem in image and point clouds registration, with scale problem is defined by the optimal corresponding between the image resolution and the density of point clouds. Test dataset includes the DMC images and point clouds acquired by the Leica ALS50 II over an area in Henan Prov., China. Main contributions of the paper includes: [1] an derived collinear equation is introduced by which a ground point is expressed by its parametric form, which makes it possible to replace point feature by linear feature, hence avoiding the problem that it is almost impossible to find a point in the point clouds which is accurately corresponds to a point in the image space; [2] least square method is used to calculate the registration transformation parameters and the unknown parameter λ in the same time;[3] scale problem is analyzed semi-quantitatively and to the authors' best knowledge, it is the first time in literature that clearly defines the scale problem and carries out semi-quantitative analysis in the context of LiDAR data processing.

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Registration of aerial imagery and aerial LiDAR data using centroids of plane roof surfaces as control information

Cited by 41 publications

References 2 publications

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

A Review of Optical Imagery and Airborne LiDAR Data Registration Methods

The Direct Registration of Lidar Point Clouds and High Resolution Image Based on Linear Feature by Introducing an Unknown Parameter

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