ORCID Profile
0000-0001-9894-3119
Current Organisation
Curtin University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Copernicus GmbH
Date: 15-06-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLI-B5-477-2016
Abstract: 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: Elsevier BV
Date: 11-2014
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: American Society for Photogrammetry and Remote Sensing
Date: 09-2012
Publisher: Copernicus GmbH
Date: 29-05-2019
DOI: 10.5194/ISPRS-ANNALS-IV-2-W5-575-2019
Abstract: Abstract. With underwater photogrammetric mapping becoming more prominent due to the lower costs for waterproof cameras as well as lower costs for underwater platforms, the aim of this research is to investigate chromatic aberration in underwater environments. Chromatic aberration in in-air applications is to be known to systematically influence the observations of up to a few pixels. In order to achieve pixel-level positioning accuracy, this systematic influence needs further investigation. However, while chromatic aberration studies have been performed for in-air environments, there is a lack of research to quantify the influence of chromatic aberration in underwater environments. Using images captured in a water tank from three different GoPro cameras in five datasets, we investigate possible chromatic aberration by running two different adjustments on the extracted red (R), green (G) and blue (B) bands. The first adjustment is an adjustment that calculates the interior orientation parameters for each set of images independently in a free network adjustment. The second adjustment solves for all interior orientation parameters (for R, G, and B channels) in a combined adjustment per camera, constraining the point observations in object space. We were able to quantify significant chromatic aberrations in our evaluation, with the largest aberrations observed for red band followed by green and blue.
Publisher: Informa UK Limited
Date: 02-2014
Publisher: Frontiers Media SA
Date: 10-04-2017
Publisher: Copernicus GmbH
Date: 15-06-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLI-B5-405-2016
Abstract: 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: 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: 02-05-2013
DOI: 10.5194/ISPRSARCHIVES-XL-1-W1-133-2013
Abstract: Abstract. Exploration of various places using low-cost camera solutions over decades without having a photogrammetric application in mind has resulted in large collections of images and videos that may have significant cultural value. The purpose of collecting this data is often to provide a log of events and therefore the data is often unstructured and of varying quality. Depending on the equipment used there may be approximate location data available for the images but the accuracy of this data may also be of varying quality. In this paper we present an approach that can deal with these conditions and process datasets of this type to produce 3D models. Results from processing the dataset collected during the discovery and subsequent exploration of the HMAS Sydney and HSK Kormoran wreck sites shows the potential of our approach. The results are promising and show that there is potential to retrieve significantly more information from many of these datasets than previously thought possible.
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: MDPI AG
Date: 15-09-2020
DOI: 10.3390/RS12183002
Abstract: The number of researchers utilising imagery for the 3D reconstruction of underwater natural (e.g., reefs) and man-made structures (e.g., shipwrecks) is increasing. Often, the same procedures and software solutions are used for processing the images as in-air without considering additional aberrations that can be caused by the change of the medium from air to water. For instance, several publications mention the presence of chromatic aberration (CA). The aim of this paper is to investigate CA effects in low-cost camera systems (several GoPro cameras) operated in an underwater environment. We found that underwater and in-air distortion profiles differed by more than 1000 times in terms of maximum displacement and in terms of curvature. Moreover, significant CA effects were found in the underwater profiles that did not exist in-air. Furthermore, the paper investigates the effect of adjustment constraints imposed on the underwater self-calibration and the reliability of the interior orientation parameters. The analysis of the precision shows that in-air RMS values are just due to random errors. In contrast, the underwater calibration RMS values are 3x-6x higher than the exterior orientation parameter (EOP) precision, so these values contain both random error and the systematic effects from the CA. The accuracy assessment shows significant differences.
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/RJ14072
Abstract: Current methods to measure aboveground biomass (AGB) do not deliver adequate results in relation to the extent and spatial variability that characterise rangelands. An optimised protocol for the assessment of AGB is presented that enables calibration and validation of remote-sensing imagery or plant growth models at suitable scales. The protocol combines a limited number of destructive s les with non-destructive measurements including normalised difference vegetation index (NDVI), canopy height and visual scores of AGB. A total of 19 sites were s led four times during two growing seasons. Fresh and dry matter weights of dead and green components of AGB were recorded. Similarity of responses allowed grouping into Open plains sites dominated by annual grasses, Bunch grass sites dominated by perennial grasses and Spinifex (Triodia spp.) sites. Relationships between non-destructive measurements and AGB were evaluated with a simple linear regression per vegetation type. Multiple regression models were first used to identify outliers and then cross-validated using a ‘Leave-One-Out’ and ‘Leave-Site-Out’ (LSO) approach on datasets including and excluding the identified outliers. Combining all non-destructive measurements into one single regression model per vegetation type provided strong relationships for all seasons for total and green AGB (adjusted R2 values of 0.65–0.90) for datasets excluding outliers. The model provided accurate assessments of total AGB in heterogeneous environments for Bunch grass and Spinifex sites (LSO-Q2 values of 0.70–0.88), whereas assessment of green AGB was accurate for all vegetation types (LSO-Q2 values of 0.62–0.84). The protocol described can be applied at a range of scales while considerably reducing s ling time.
Publisher: Copernicus GmbH
Date: 27-07-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B4-67-2012
Abstract: Abstract. As a consequence of the wide-spread application of digital geo-data in geographic information systems (GIS), quality control has become increasingly important to enhance the usefulness of the data. For economic reasons a high degree of automation is required for the quality control process. This goal can be achieved by automatic image analysis techniques. An ex le of how this can be achieved in the context of quality assessment of cropland and grassland GIS objects is given in this paper. The quality assessment of these objects of a topographic dataset is carried out based on multi-temporal information. The multi-temporal approach combines the channels of all available images as a multilayer image and applies a pixel-based SVM-classification. In this way multispectral as well as multi-temporal information is processed in parallel. The features used for the classification consist of spectral, textural (Haralick features) and structural (features derived from a semi-variogram) features. After the SVM-classification, the pixel-based result is mapped to the GIS-objects. Finally, a simple ruled-based approach is used in order to verify the objects of a GIS database. The approach was tested using a multi-temporal data set consisting of one 5-channel RapidEye image (GSD 5 m) and two 3-channel Disaster Monitoring Constellation (DMC) images (GSD 32 m). All images were taken within one year. The results show that by using our approach, quality control of GIS- cropland and grassland objects is possible and the human operator saves time using our approach compared to a completely manual quality assessment.
Publisher: IEEE
Date: 07-2013
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: Wiley
Date: 07-04-2023
DOI: 10.1111/JOA.13872
Abstract: Three‐dimensional (3D) representations of anatomical specimens are increasingly used as learning resources. Photogrammetry is a well‐established technique that can be used to generate 3D models and has only been recently applied to produce visualisations of cadaveric specimens. This study has developed a semi‐standardised photogrammetry workflow to produce photorealistic models of human specimens. Eight specimens, each with unique anatomical characteristics, were successfully digitised into interactive 3D models using the described workflow and the strengths and limitations of the technique are described. Various tissue types were reconstructed with apparent preservation of geometry and texture which visually resembled the original specimen. Using this workflow, an institution could digitise their existing cadaveric resources, facilitating the delivery of novel educational experiences.
Publisher: Wiley
Date: 03-2019
DOI: 10.1111/PHOR.12275
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2017
Publisher: Copernicus GmbH
Date: 07-09-2012
DOI: 10.5194/ISPRSARCHIVES-XXXVIII-4-W19-251-2011
Abstract: Abstract. In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of the two most frequent classes: cropland and grassland. For our experiments three categories of imagery, high resolution aerial images, high resolution RapidEye satellite images and medium resolution Disaster Monitoring Constellation (DMC) satellite images are examined. An object-based image classification, as one of the most reliable methods for the automatic updating and evaluation of landuse geospatial databases, is chosen. The object boundaries are taken from a GIS database, each object is described by means of a set of image based features. Spectral, textural and structural (semivariogram derived) features are extracted from images of different dates and sensors. During classification a supervised decision trees generating algorithm is applied. To evaluate the potential of the different images, all possible combinations of the available image data are tested during classification. The results show that the best performance of landuse classification is based on RapidEye data (overall accuracy of 90%), obtaining slightly accuracy increases when this imagery is combined with additional image data (overall accuracy of 92%).
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: Copernicus GmbH
Date: 04-06-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W13-27-2019
Abstract: Abstract. The ersity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.
Publisher: Springer Science and Business Media LLC
Date: 04-07-2017
Publisher: Copernicus GmbH
Date: 02-05-2013
DOI: 10.5194/ISPRSARCHIVES-XL-1-W1-151-2013
Abstract: Abstract. 3D city models are used in many fields. Photorealistic building textures find applications such as façade reconstruction, thermal building inspections and heat leakage detection using thermal infrared (TIR) images, quantitative evaluation or study of the materials lying on the object’s surface using multispectral images. Often texturing cannot be done using the same data which was used for 3D reconstruction or textures have to be updated. In such cases co-registration between 3D building models and images has to be carried out. In this paper we present a method for model-to-image matching and texture extraction with best texture selection procedure. We present results for two data sets, first for TIR image sequences taken from a helicopter and second for VIS images taken from an Unmanned Aerial Vehicle (UAV).
Publisher: Copernicus GmbH
Date: 19-09-2014
DOI: 10.5194/ISPRSANNALS-II-7-47-2014
Abstract: Abstract. The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were ided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites (2) separate relationships per site group (3) multiple regressions including selected vegetation indices per site group and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.
Publisher: Elsevier BV
Date: 07-2017
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: MDPI AG
Date: 11-04-2022
DOI: 10.3390/RS14081831
Abstract: Lake Sawa located in Southwest Iraq is a unique natural landscape and without visible inflow and outflow from its surrounding regions. Investigating the environmental and physical dynamics and the hydrological changes in the lake is crucial to understanding the impact of hydrological changes, as well as to inform planning and management in extreme weather events or drought conditions. Lake Sawa is a saltwater lake, covering about 4.9 square kilometers at its largest in the 1980s. In the last decade, the lake has dried out, shrinking to less than 75% of its average size. This contribution focuses on calculating the bank erosion and accretion of Lake Sawa utilizing remote sensing data captured by Landsat platforms (1985–2020). The methodology was validated using higher-resolution Sentinel imagery and field surveys. The outcomes indicated that the area of accretion is significantly higher than erosion, especially of the lake’s banks in the far north and the south, in which 1.31 km2 are lost from its surface area. Further analysis of especially agricultural areas around the lake have been performed to better understand possible reasons causing droughts. Investigations revealed that one possible reason behind droughts is related to the rapid increase in agriculture areas surrounding the lake. It has been found that the agriculture lands have expanded by 475% in 2020 compared to 2010. Linear regression analysis revealed that there is a high correlation (69%) between the expanding of agriculture lands and the drought of Lake Sawa.
Publisher: Springer Science and Business Media LLC
Date: 12-2019
Publisher: Springer Science and Business Media LLC
Date: 03-05-2017
Publisher: Schweizerbart
Date: 08-2016
No related grants have been discovered for Petra Helmholz.