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In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III / 4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach. [ABSTRACT FROM AUTHOR]

This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems. [ABSTRACT FROM PUBLISHER]

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z -score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks. [ABSTRACT FROM AUTHOR]

Highlights • Two robust cylinder fitting algorithms are devised in laser scanning point clouds. • The new methods fit robust cylinder in the presence of high percentage of outliers. • The methods reliably fit partially and fully scanned cylinders. • The proposed methods are efficient for various sizes of cylinder fitting. • The developed methods can fit cylinders with unequal radii at their ends. Abstract Cylinders play a vital role in representing geometry of environmental and man-made structures. Most existing cylinder fitting methods perform well for outlier free data sampling a full cylinder, but are not reliable in the presence of outliers or incomplete data. Point Cloud Data (PCD) are typically outlier contaminated and incomplete. This paper presents two robust cylinder fitting algorithms for PCD that use robust Principal Component Analysis (PCA) and robust regression. Experiments with simulated and real data show that the new methods are efficient (i) in the presence of outliers, (ii) for partially and fully sampled cylinders, (iii) for small and large numbers of points, (iv) for various sizes: radii and lengths, and (v) for cylinders with unequal radii at their ends. A simulation study consisting of 1000 cylinders of 1 m radius with 20% clustered outliers, reveals that a PCA based method fits cylinders with an average radius of 2.84 m and with a principal axis biased by outliers of 9.65° on average, whereas the proposed robust method correctly estimates the average radius of 1 m with only 0.27° bias angle in the principal axis. [ABSTRACT FROM AUTHOR]