Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

Parmehr, Ebadat G.; Fraser, Clive S.; Zhang, Chunsun; Leach, J. H.

doi:10.1016/j.isprsjprs.2013.11.015

Cited by 91 publications

(69 citation statements)

References 38 publications

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“…These data sets were registered using the mutual information based method [34], which exploits the statistical dependency between same-and multi-modal data sets to produce a similarity measure and uses LiDAR intensity in simultaneous registration. Nevertheless, the building roofs and tree tops are considerably displaced with respect to the LiDAR owing to the absence of true orthophotos.…”

Section: Test Datamentioning

confidence: 99%

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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Abstract:The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m 2 ), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over-and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.

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Section: Test Datamentioning

confidence: 99%

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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“…Mutual information, as the most successful example, has been proven to be effective for the optical and elevation data registration as demonstrated in [14].…”

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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“…In order to consider existing geometric dependence between a pair of variables, Normalised Combined Mutual Information (NCMI) has been adopted for registration of optical imagery with LiDAR data (Parmehr et al, 2014). The NCMI of a variable C and two already aligned variables A and B is defined as…”

Section: Mutual Informationmentioning

confidence: 99%

Optimal parameter selection for intensity-based multi-sensor data registration

Parmehr

Fraser

Zhang

et al. 2014

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

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ABSTRACT:Accurate co-registration of multi-sensor data is a primary step in data integration for photogrammetric and remote sensing applications. A proven intensity-based registration approach is Mutual Information (MI). However the effectiveness of MI for automated registration of multi-sensor remote sensing data can be impacted to the point of failure by its non-monotonic convergence surface. Since MI-based methods rely on joint probability density functions (PDF) for the datasets, errors in PDF estimation can directly affect the MI value. Certain PDF parameter values, such as the bin-size of the joint histogram and the smoothing kernel, need to be assigned in advance, since they play a key role in forming the convergence surface. The lack of a general approach to the assignment of these parameter values for various data types reduces both the automation level and the robustness of registration. This paper proposes a new approach for selection of optimal parameter values for PDF estimation in MI-based registration of optical imagery to LiDAR point clouds. The proposed method determines the best parameters for PDF estimation via an analysis of the relationship between similarity measure values of the data and the adopted geometric transformation in order to achieve the optimal registration reliability. The performance of the proposed parameter selection method is experimentally evaluated and the obtained results are compared with those achieved through a feature-based registration method.

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Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

Cited by 91 publications

References 38 publications

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

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

Optimal parameter selection for intensity-based multi-sensor data registration

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