Proposed segmentation method METHODOLOGY

Points are labelled as belonging to a flat surface, ridge, peak, etc. Rabbani et al. 2006 investigate surface roughness to segment planar and curved surfaces. However, their methods are not useful for the point clouds of the electrical substations where there are mainly planar surfaces and not many curved surfaces. Tovari and Pfeifer 2005 present a region growing method that utilizes the similarity of normal vectors and the distance of a point to the best fit plane as the growing criteria. Some researchers employed PCA in their segmentation algorithms. Belton and Lichti 2006 segment boundary, edge and surface points by investigating the eigenvalues of points that are obtained by PCA. El-Halawany et al. 2012 also employ a region growing method to recognize poles in an urban environment by analysing eigenvectors and eigenvalues that are achieved by PCA. This can be beneficial to upcoming stages of our research when other objects, like rods and poles, are to be recognized. Al-Durgham and Habib 2013 segment the planar surfaces in which the quality of input LiDAR data is taken into consideration. Point density and accuracy are two measures of the data quality used for the segmentation. Considering the data quality in the segmentation algorithm is innovative since it is not usually incorporated in the existing segmentation methods. Datasets are usually heterogeneous with varying point density and accuracy as they are resulting from the integration of many smaller datasets with different point density and accuracy. This can be useful in this research since the datasets were collected by terrestrial laser scanners, so variations in point density are expected. Therefore, an investigation of the point density and its potential effects on the results can be pursued as a future work. Brodu and Lague 2012 inspect‎ the‎ 3D‎ structure‎ of‎ points’‎ neighbourhood to segment the point clouds of complicated natural scenes. The points’‎neighbourhood‎are‎determined‎to‎be‎ distributed in 1D, 2D or 3D by investigating the local behaviour of point cloud at different scales. Although our proposed method‎ also‎ inspects‎ the‎ structure‎ of‎ points’‎ neighbourhood,‎ objects in electrical substations are man-made, parameterized objects that are totally different from objects usually found in natural scenes. Many of region growing methods are highly dependent on seed point selection and an error in seed point selection can have a negative impact on the segmentation results Besl and Jain, 1988. Parametric-domain segmentation methods are composed of two steps: assigning an attribute to each point and then clustering labelled points that usually takes advantage of an accumulator array. Many researchers segment planar surfaces using parametric-domain methods e.g. Lari et al. 2012, Filin and Pfeifer 2005, Vosselman and Dijkman 2001, Kim and Habib 2007 and Maas and Vosselman 1999 who segment planes in urban environments. Roggero 2002 segments an urban environment by employing PCA in attribute space. The primary restriction of the parametric-domain methods is that they are computationally intense and thus not pertinent for very large datasets like datasets used in this research. Some researchers have concentrated on different aspects of the planar surface segmentation like the size and properties of points’‎neighbourhood‎which‎are also taken into consideration in this paper. In fact, planar surfaces are segmented by investigation‎of‎points’‎neighbourhood‎structure‎that‎can have a great impact on the segmentation results. Bae et al. 2005 propose an approach to determine an optimal neighbourhood size for points by minimizing the variance of the estimated normal vector. Some researchers propose new neighbourhood definitions to improve their segmentation algorithms. Filin and Pfeifer 2005 investigate the shortcomings of existing neighbourhood definitions e.g. TIN, rasterization and spherical neighbourhood. Then they propose adaptive cylinder neighbourhood that considers both the 3D relationships between points and the physical shape of the surfaces. Kim and Habib 2007 utilize cylinder neighbourhood concept for the segmentation of planar patches using parametric-domain methods successfully. Filin and Pfeifer 2006, Lari et al. 2012 and Lari and Habib 2013 improve their planar surface segmentation results by considering the noise level and the physical shape of the associated surface. However, all mentioned papers take advantage of the parametric-domain methods that are computationally not efficient.

3. METHODOLOGY

This section is composed of two parts; section 3.1 details the proposed method for the segmentation of planar surfaces in LiDAR point cloud of an electrical substation. Section 3.2 describes how PCA is employed to segment planar surfaces in the same electrical substation. Afterwards, the performance of our proposed segmentation method is compared with the performance of PCA in section ‎5.

3.1 Proposed segmentation method

The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood distribution direction. Many methods can be employed to determine the distribution direction of a point’s neighbourhood in which a dispersion matrix is usually defined and analysed. Our proposed method incorporates defining a new dispersion matrix which is further analysed to determine the‎ distribution‎ direction‎ of‎ each‎ point’s‎ neighbourhood. To create the proposed dispersion matrix ,‎ each‎ point’s‎ neighbourhood is projected in nine different directions in space. These directions incorporates the three cardinal directions X, Y and Z axes and two directions in each of the XY, XZ and YZ planes at 45° to the cardinal axes, portrayed in Figure 1.            Dispersion z Ra nge zy Ra nge zx Ra nge yz Ra nge y Ra nge yx Ra nge xz Ra nge xy Ra nge x Ra nge 1 Where: x Ra nge = maximum value of projected points on axis X – minimum value of projected points on axis X etc. The data extents along these nine directions constitute elements of the proposed dispersion matrix, indicted above. The neighbourhood of a point on a plane would have a large 45° X X Y Y Z Z XY YX XZ ZX YZ ZY 45° 45° Figure 1. Three Cartesian and six non-Cartesian axes on which points are projected ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-5-55-2014 56 distribution in two orthogonal directions on the plane and almost no distribution in the plane ’s normal direction. Since almost all planar surfaces in the electrical substations are vertical planes, one of the principal distribution directions would be the vertical direction. This corresponds to a large value for Rangez in the dispersion matrix. In addition to the vertical direction, the neighbourhood of a point on a plane would have a large distribution in another direction that is orthogonal to the vertical direction and the plane ’s normal direction. If for instance the neighbourhood of a point exhibits a large distribution in Z and X axes and almost no distribution along Y axis, this point is considered to lie on a plane. This corresponds to large values of Range z and Range x and very small magnitude of Range y. Once a point with planar neighbourhood is identified, it will be labelled accordingly. Later on, points with planar neighbourhood that are in a certain distance from each other are aggregated in a segment. It should be noted that the proposed segmentation algorithm has successfully been employed to decompose complex structures in an electrical substation into simpler homogeneous objects and also to recognize insulators in the electrical substation Arastounia and Lichti, 2013. Insulators can also be recognized by photogrammetric methods Armeshi and Habib, 2013. 3.2 Principal component analysis PCA is one of the most common methods used in the segmentation of LiDAR point clouds. In PCA, the eigenvectors and the eigenvalues of the covariance matrix of each point’s‎ neighbourhood are used to segment planar surfaces.              2 2 2 matrix Covariance z zy zx yz y yx xz xy x          2 Where: 2 1 2 2 2 1 var x x k x E x E x k i i x         3         k i i i xy y y x x k y E x E xy E y x 1 1 , cov  4 In PCA if two of the eigenvalues are roughly of the same magnitude and the third eigenvalue is very close to zero, the neighbourhood of the point under study is deemed to have a planar distribution. All points in the neighbourhood of each point are used to create the covariance matrix. The eigenvalues of the covariance matrix is then computed and if two of the normalized eigenvalues have almost same magnitude and the third normalized eigenvalue is almost zero, the point under study is considered to have a planar neighbourhood. The normalized eigenvalues are computed as:    3 1 i i i i n    5 Where: eigenvalue ith : i  eigenvalue normalized ith : i n  The following is the mathematical expression of the criteria utilized to detect points with planar neighbourhood using PCA: and 3 2 1      n n n 6

4. DATASETS