ORCID Profile
0000-0002-2879-7918
Current Organisation
Curtin University
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Photogrammetry and Remote Sensing | Curatorial and Related Studies | Heritage and Cultural Conservation | Museum Studies
Conserving Intangible Cultural Heritage | Computer Software and Services not elsewhere classified |
Publisher: Elsevier BV
Date: 05-2019
Publisher: Copernicus GmbH
Date: 04-06-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W13-589-2019
Abstract: Abstract. With recent advancements in UAV based technology the use of airborne photogrammetry and LiDAR poses a new and effective approach for continuous, fast and efficient beach monitoring surveys. This paper aims to compare three platforms (a DJI Phantom Pro 4 using Ground Control Points, a DJI Matrice 200 with built in PPK allowing direct georeferencing and a DJI Matrice 600 with a Riegl Mini-VUX LiDAR system) in order to assess if they enable beach surveys to be performed efficiently, accurately and cost- effectively. A series of beach surveys were performed over a period of 6 months enabling the ability of each UAV surveying technique to be assessed for the identification and evaluation of trends in the changing topography of beaches and shorelines. The study area (Warnbro Sound, Western Australia) is an area that has experienced significant coastal change over the last 20 years as well as several serious weather events in the course of this research. The results show a significant positive bias of a consistent vertical offset to the ground surface by 4–9 cm between the two image based systems in comparison to the LiDAR system. Although these height offsets are significant it is still within the accuracy required to perform successful beach surveys, and all systems were able to quantify the change of the beach shoreline in area (m2) and volume (m3).
Publisher: Informa UK Limited
Date: 02-2014
Publisher: Informa UK Limited
Date: 12-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: IEEE
Date: 03-2014
Publisher: Copernicus GmbH
Date: 26-09-2018
DOI: 10.5194/ISPRS-ARCHIVES-XLII-1-217-2018
Abstract: Abstract. As more cities are starting to experience the urban heat islands effect, knowledge about the energy emitted from building roofs is of primary importance. Since this energy depends both on roof orientations and materials, we tackled both issues by analysing sensor data from multispectral, thermal infrared, high-resolution RGB, and airborne laser datasets (each with different spatial resolutions) of a council in Perth, Australia. To localise the roofs, we acquired building outlines that had to be updated using the normalised digital surface model, the NDVI and the planarity. Then, we computed a semantic 3D model of the study area, with roof detail analysis being a particular focus. The main objective of this study, however, was to classify three commonly used roofing materials: Cement tiles, Colorbond and Zincalume by combining the multispectral and thermal infrared image bands while the high-resolution RGB dataset was used to provide additional information about the roof texture. Three types of image segmentation approaches were evaluated to assess any differences while performing the material classification pixel-wise, superpixel-wise and building-wise image segmentation. Due to the limited amount of labelled data, we extended the dataset by labelling data ourselves and merged Colorbond and Zincalume into one separate class. The supervised classifier Random Forest was applied to all reasonable configurations of segmentation kinds, numbers of classes, and finally, keeping track of the added value of principal component analysis.
Publisher: Copernicus GmbH
Date: 10-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B4-63-2016
Abstract: In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.
Publisher: Copernicus GmbH
Date: 15-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B5-477-2016
Abstract: Modern digital cameras are increasing in quality whilst decreasing in size. In the last decade, a number of waterproof consumer digital cameras (action cameras) have become available, which often cost less than $500. A possible application of such action cameras is in the field of Underwater Photogrammetry. Especially with respect to the fact that with the change of the medium to below water can in turn counteract the distortions present. The goal of this paper is to investigate the suitability of such action cameras for underwater photogrammetric applications focusing on the stability of the camera and the accuracy of the derived coordinates for possible photogrammetric applications. For this paper a series of image sequences was capture in a water tank. A calibration frame was placed in the water tank allowing the calibration of the camera and the validation of the measurements using check points. The accuracy assessment covered three test sets operating three GoPro sports cameras of the same model (Hero 3 black). The test set included the handling of the camera in a controlled manner where the camera was only dunked into the water tank using 7MP and 12MP resolution and a rough handling where the camera was shaken as well as being removed from the waterproof case using 12MP resolution. The tests showed that the camera stability was given with a maximum standard deviation of the camera constant σc of 0.0031mm for 7MB (for an average c of 2.720mm) and 0.0072 mm for 12MB (for an average c of 3.642mm). The residual test of the check points gave for the 7MB test series the largest rms value with only 0.450mm and the largest maximal residual of only 2.5 mm. For the 12MB test series the maximum rms value is 0. 653mm.
Publisher: Copernicus GmbH
Date: 16-10-2013
DOI: 10.5194/ISPRSANNALS-II-5-W2-43-2013
Abstract: Abstract. While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications. In addition to the issues typically associated with processing point data, such applications raise a number of new complications, such as the complexity of the scenes scanned, along with the sheer volume of data. Consequently, automated procedures are required for processing, and analysing such data. This paper introduces a method for modelling multi-modal, geometrically complex objects in terrestrial laser scanning point data specifically, the modelling of trees. The model method comprises a number of geometric features in conjunction with a multi-modal machine learning technique. The model can then be used for contextually dependent region growing through separating the tree into its component part at the point level. Subsequently object analysis can be performed, for ex le, performing volumetric analysis of a tree by removing points associated with leaves. The workflow for this process is as follows: isolate in idual trees within the scanned scene, train a Gaussian mixture model (GMM), separate clusters within the mixture model according to exemplar points determined by the GMM, grow the structure of the tree, and then perform volumetric analysis on the structure.
Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 05-2019
Publisher: Copernicus GmbH
Date: 16-10-2013
DOI: 10.5194/ISPRSANNALS-II-5-W2-217-2013
Abstract: Abstract. A new robust way for ground surface extraction from mobile laser scanning 3D point cloud data is proposed in this paper. Fitting polynomials along 2D/3D points is one of the well-known methods for filtering ground points, but it is evident that unorganized point clouds consist of multiple complex structures by nature so it is not suitable for fitting a parametric global model. The aim of this research is to develop and implement an algorithm to classify ground and non-ground points based on statistically robust locally weighted regression which fits a regression surface (line in 2D) by fitting without any predefined global functional relation among the variables of interest. Afterwards, the z (elevation)-values are robustly down weighted based on the residuals for the fitted points. The new set of down weighted z-values along with x (or y) values are used to get a new fit of the (lower) surface (line). The process of fitting and down-weighting continues until the difference between two consecutive fits is insignificant. Then the final fit represents the ground level of the given point cloud and the ground surface points can be extracted. The performance of the new method has been demonstrated through vehicle based mobile laser scanning 3D point cloud data from urban areas which include different problematic objects such as short walls, large buildings, electric poles, sign posts and cars. The method has potential in areas like building/construction footprint determination, 3D city modelling, corridor mapping and asset management.
Publisher: Informa UK Limited
Date: 15-01-2019
Publisher: Copernicus GmbH
Date: 15-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B5-405-2016
Abstract: Multispectral analysis is a widely used technique in the photogrammetric and remote sensing industry. The use of Terrestrial Laser Scanning (TLS) in combination with imagery is becoming increasingly common, with its applications spreading to a wider range of fields. Both systems benefit from being a non-contact technique that can be used to accurately capture data regarding the target surface. Although multispectral analysis is actively performed within the spatial sciences field, its extent of application within an archaeological context has been limited. This study effectively aims to apply the multispectral techniques commonly used, to a remote Indigenous site that contains an extensive gallery of aging rock art. The ultimate goal for this research is the development of a systematic procedure that could be applied to numerous similar sites for the purpose of heritage preservation and research. The study consisted of extensive data capture of the rock art gallery using two different TLS systems and a digital SLR camera. The data was combined into a common 2D reference frame that allowed for standard image processing to be applied. An unsupervised k-means classifier was applied to the multiband images to detect the different types of rock art present. The result was unsatisfactory as the subsequent classification accuracy was relatively low. The procedure and technique does however show potential and further testing with different classification algorithms could possibly improve the result significantly.
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 04-2016
Publisher: Copernicus GmbH
Date: 13-09-2017
DOI: 10.5194/ISPRS-ANNALS-IV-2-W4-115-2017
Abstract: Abstract. The least square plane fitting adjustment method has been widely used for registration of the mobile laser scanning (MLS) point clouds. The inputs for this process are the plane parameters and points of the corresponding planar features. These inputs can be manually and/or automatically extracted from the MLS point clouds. A number of papers have been proposed to automatically extract planar features. They use different criteria to extract planar features and their outputs are slightly different. This will lead to differences in plane parameters values and points of the corresponding features. This research studies and compares the results of the least square plane fitting adjustment process with different inputs obtained by using different segmentation methods (e.g. RANSAC, RDPCA, Cabo, RGPL) and the results from the point to plane approach – an ICP variant. The questions for this research are: (1) which is the more suitable method for registration of MLS sparse point clouds and (2) which is the best segmentation method to obtain the inputs for the plane based MLS point clouds registration? Experiments were conducted with two real MLS point clouds captured by the MDL – Dynascan S250 system. The results show that ICP is less accurate than the least square plane fitting adjustment. It also shows that the accuracy of the plane based registration process is highly correlated with the mean errors of the extracted planar features and the plane parameters. The conclusion is that the RGPL method seems to be the best methods for planar surfaces extraction in MLS sparse point clouds for the registration process.
Publisher: Copernicus GmbH
Date: 23-04-2014
DOI: 10.5194/ISPRSARCHIVES-XL-4-335-2014
Abstract: Abstract. The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2009
Publisher: Wiley
Date: 03-2019
DOI: 10.1111/PHOR.12275
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2017
Publisher: IEEE
Date: 05-2013
DOI: 10.1109/CRV.2013.28
Publisher: Springer Science and Business Media LLC
Date: 04-07-2017
Publisher: Copernicus GmbH
Date: 09-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B3-351-2016
Abstract: The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.
Publisher: Elsevier BV
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: Copernicus GmbH
Date: 16-10-2013
DOI: 10.5194/ISPRSANNALS-II-5-W2-61-2013
Abstract: Abstract. At the end of the first quarter of 2012, more than 600 Velodyne LiDAR systems had been sold worldwide for various robotic and high-accuracy survey applications. The ultra-compact Velodyne HDL-32E LiDAR has become a predominant sensor for many applications that require lower sensor size/weight and cost. For high accuracy applications, cost-effective calibration methods with minimal manual intervention are always desired by users. However, the calibrations are complicated by the Velodyne LiDAR's narrow vertical field of view and the very highly time-variant nature of its measurements. In the paper, the temporal stability of the HDL-32E is first analysed as the motivation for developing a new, automated calibration method. This is followed by a detailed description of the calibration method that is driven by a novel segmentation method for extracting vertical cylindrical features from the Velodyne point clouds. The proposed segmentation method utilizes the Velodyne point cloud's slice-like nature and first decomposes the point clouds into 2D layers. Then the layers are treated as 2D images and are processed with the Generalized Hough Transform which extracts the points distributed in circular patterns from the point cloud layers. Subsequently, the vertical cylindrical features can be readily extracted from the whole point clouds based on the previously extracted points. The points are passed to the calibration that estimates the cylinder parameters and the LiDAR's additional parameters simultaneously by constraining the segmented points to fit to the cylindrical geometric model in such a way the weighted sum of the adjustment residuals are minimized. The proposed calibration is highly automatic and this allows end users to obtain the time-variant additional parameters instantly and frequently whenever there are vertical cylindrical features presenting in scenes. The methods were verified with two different real datasets, and the results suggest that up to 78.43% accuracy improvement for the HDL-32E can be achieved using the proposed calibration method.
Publisher: Copernicus GmbH
Date: 30-05-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-1-W1-75-2017
Abstract: Abstract. 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.
Publisher: Informa UK Limited
Date: 02-07-2016
Publisher: Copernicus GmbH
Date: 16-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B5-653-2016
Abstract: The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage.
Publisher: IEEE
Date: 12-2012
Publisher: Copernicus GmbH
Date: 20-07-2012
DOI: 10.5194/ISPRSANNALS-I-3-269-2012
Abstract: Abstract. This paper investigates the problem of local surface reconstruction and best fitting for planar surfaces from unorganized 3D point cloud data. Least Squares (LS), its equivalent Principal Component Analysis (PCA) and RANSAC are the three most popular techniques for fitting planar surfaces to 3D data. LS and PCA are sensitive to outliers and do not give reliable and robust parameter estimation. The RANSAC algorithm is robust but it is not completely free from the effect of outliers and is slow for large datasets. In this paper, we propose a diagnostic-robust statistical algorithm that uses both diagnostics and robust approaches in combination for fitting planar surfaces in the presence of outliers. Recently introduced high breakdown and fast Minimum Covariance Determinant (MCD) based location and scatter estimates are used for robust distance to identify outliers and a MCD based robust PCA approach is used as an outlier resistant technique for plane fitting. The benefits of the new diagnostic-robust algorithm are demonstrated with artificial and real laser scanning point cloud datasets. Results show that the proposed method is significantly better and more efficient than the other three methods for planar surface fitting. This method also has great potential for robust local normal estimation and for other surface shape fitting applications.
Publisher: Schweizerbart
Date: 08-2016
Start Date: 01-2020
End Date: 01-2024
Amount: $461,783.00
Funder: Australian Research Council
View Funded Activity