Ben Gorte | The University of New South Wales (original) (raw)

Papers by Ben Gorte

Research paper thumbnail of Article Evaluating MERIS-Based Aquatic Vegetation Mapping

Research paper thumbnail of Improving Geo-Information Reliability by Centralized Change Detection Management

A consortium called Mutatis Mutandis (MutMut), consisting of three Universities and eight produce... more A consortium called Mutatis Mutandis (MutMut), consisting of three Universities and eight producers and users of geo-information, was established in the Netherlands to streamline change detection on a national level. After preliminary investigations concerning market feasibility, three actions are being taken by MutMut: (1) construction of a centralised, web based repository for collection and exchange of changes detected by various actors, (2) design and implementation of an image based, automated change detection methodology, and (3) widening the scope of the project in a European context. This paper focusses on the second action. Objects in the database are used to formulate hypotheses, for which newer imagery may or may not provide evidence. In the latter case, older imagery is considered as well. If evidence for the hypothesis could still be found there, a possible change is detected. The paper shows preliminary results of a first implementation of this methodology. 1.

Research paper thumbnail of Influences of Vegetation on Laser Altimetry – Analysis and Correction Approaches. The Int

The influences of low vegetation on airborne laser scanning are studied. High vegetation is remov... more The influences of low vegetation on airborne laser scanning are studied. High vegetation is removed by filtering, but low vegetation causes systematic errors in digital terrain models. Many researchers have reported that the measurements are too high. The investigation of influences on the laser range measurement improves the understanding of the technology in use and gives explanations for the observed errors. The possibilities of correcting the data with information on the vegetation type are studied, using ground truth data from terrestrial measurements as reference. An alternative approach using texture measures, which does not require information on the land cover type, is presented. Texture has previously been defined for digital images and its equivalent for point clouds is presented here. 1

Research paper thumbnail of Height Texture of Low Vegetation in Airborne Laser Scanner Data and Its Potential for DTM Correction

The influence of low vegetation on laser scanning and its disturbing effect (a systematic positiv... more The influence of low vegetation on laser scanning and its disturbing effect (a systematic positive height shift) during DTM generation is well recognized. Based on our experience in a previous study of estimating the effect of medium-height vegetation (shrubs and bushes) in rough terrain by using point-cloud based co-occurrence texture methods, we now investigate the effect of lower vegetation, such as grass, in very flat marshland conditions. When vegetation is very low, and thus the effect very small, it appears difficult to separate the systematic shift from random measurement noise. When the shift becomes larger, however, it shows significant correlation with texture measures such as slope texture and standard deviation. These measures are derived from the laser data itself and do not require any additional information.

Research paper thumbnail of Virtual sensors - Synthesizing dynamic crowdsensing data into information on static instances

There has been a steady increase in applications that rely on crowdsensing to gather data for ana... more There has been a steady increase in applications that rely on crowdsensing to gather data for analysis purposes. Crowdsensing enables the use of dynamic sensors to collect data on static objects of interest. However, using dynamic sensors in this way causes a problem. The focus of the collected data is on the position of the sensor, not on the object of interest. This results in difficulties in tracking the object of interest in terms of what part of the data from the dynamic sensor describes the object of interest. To shift the focus from the dynamic sensors to a static object, the virtual sensor is introduced. A virtual sensor enables the grouping of data from different dynamic sensors into a single virtual sensor based on measurement positions. The data from the multiple dynamic sensors can be analyzed to provide information per virtual sensor. The data structure of a visual sensor is close to the SensorThings API data structure, which can be expanded to support virtual sensors b...

Research paper thumbnail of Interpolation between isolines based on the Borgefors distance transform

Itc Journal, 1990

ABSTRACT

Research paper thumbnail of Robust Vehicle Tracking in Video Images Being Taken from a Helicopter

Measuring positions, velocities and accelerations/decelerations of individual vehicles in congest... more Measuring positions, velocities and accelerations/decelerations of individual vehicles in congested traffic with standard traffic monitoring equipment, such as inductive loops, are not feasible. The behavior of drivers in the different traffic situations, as re-quired for microscopic traffic flow models, is still not sufficiently known. Remote sensing and computer vision technology are recently being used to solve this problem. In this study we use video images taken from a helicopter above a fixed point of the highway. We address the problem of tracking the movement of previously detected vehicles through a stabilized video sequence. We combine two approaches, optical flow and matching based tracking, improve them by adding constraints and using scale space. Feature elements, i.e. the corners, lines, regions and outlines of each car, are extracted first. Then, optical-flow is used to find for each pixel in the interior of a car the corresponding pixel in the next image, by insertin...

Research paper thumbnail of Voxel-Based Visibility Analysis for Safety Assessment of Urban Environments

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Sep 23, 2019

Research paper thumbnail of Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Remote Sensing

Many urban applications require building polygons as input. However, manual extraction from point... more Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables t...

Research paper thumbnail of 3D Building Change Detection Between Current VHR Images and Past Lidar Data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Change detection is an essential step to locate the area where an old model should be updated. Wi... more Change detection is an essential step to locate the area where an old model should be updated. With high density and accuracy, LiDAR data is often used to create a 3D city model. However, updating LiDAR data at state or nation level often takes years. Very high resolution (VHR) images with high updating rate is therefore an option for change detection. This paper provides a novel and efficient approach to derive pixel-based building change detection between past LiDAR and new VHR images. The proposed approach aims notably at reducing false alarms of changes near edges. For this purpose, LiDAR data is used to supervise the process of finding stereo pairs and derive the changes directly. This paper proposes to derive three possible heights (so three DSMs) by exploiting planar segments from LiDAR data. Near edges, the up to three possible heights are transformed into discrete disparities. A optimal disparity is selected from a reasonable and computational efficient range centered on th...

Research paper thumbnail of On the performance of remote sensing time series reconstruction methods – A spatial comparison

Remote Sensing of Environment, 2016

Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which ... more Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which describe the temporal and spatial variability of global terrestrial vegetation, are inevitably contaminated by clouds, aerosol, snow and ice cover. In general all these conditions yield negative deviations in the time series of NDVI. Many time series reconstruction models have been developed to eliminate effect of the negative deviations and most of them perform differently in different applications and regions. The Harmonic Analysis (HA), Double logistic (DL), Asymmetric Gaussian (AG), Whittaker smoother (WS) and Savitzky–Golay filter (SG) are five of the most widely used time series reconstruction models owing to their simplicity of implementation or the capability to extract phenological metrics from the time series. The performance of these models varies with the NDVI signal and the noise distribution, however, and until now there is no consensus on which method outperforms all others under all situations. Since the NDVI signal and the noise distribution are highly dependent on regional climate and land cover, the reconstruction performance is expected to be spatially variable. Thus this study compared the five reconstruction models at pixel scale to provide practical and biome – specific recommendations for future time series reconstruction applications. Specifically, the 14 years raw daily reflectance data and ancillary Quality Assessment (QA) information from the MODIS sensor were used to generate pixel reference series and noisy series. Then the five candidate models were applied to both reference series and noisy series and three reconstruction performance metrics i.e. Overall Reconstruction Error (ORE), Fitting Related Error (FRE), and Normalized Noise Related Error (NNRE), were calculated. Finally, the performance of the five candidate reconstruction models was evaluated by applying the three metrics. The preliminary results showed that when considering ORE only, the Asymmetric Gaussian model outperforms other models over most areas of high latitude boreal region, while the Savitzky–Golay model gives the best reconstruction performance in tropical and subtropical regions. The FRE and the NNRE helped to reveal the main error sources in the reconstruction in different regions. The comparison method developed and applied in this study led to suggest adaptive selection of the best reconstruction model for specific NDVI signals and noise distribution.

Research paper thumbnail of Recent developments in the ILWIS software kernel

Research paper thumbnail of Principles of remote sensing

Research paper thumbnail of Roof Plane Extraction in Gridded Digital Surface Models

With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to t... more With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to the (application dependent) requirements, robustness, efficiency and automation of the modeling process are becoming more important than achieving the highest possible accuracy and modeling detail from the available LIDAR data. Therefore we opt for development of a 2D grid based LIDAR data analysis approach. An important step is detection and parameterization of planar surfaces (roof elements). The paper reviews four methods, based on analysis of gradients, principal components, least squares and hough transforms, respectively. It introduces a series of improvements to the standard usage of each of those methods and shows results from synthetic and real data. 1.

Research paper thumbnail of Camera-Projector 3D Scanning of a Semi-Submerged Terrain in a Flume

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013

Research paper thumbnail of Remote Sensing and Urban Analysis: GISDATA 9

... Life and Motion of Socio-economic Units (eds Andrew Frank, Jonathan Raper and JP 8:Cheylan) V... more ... Life and Motion of Socio-economic Units (eds Andrew Frank, Jonathan Raper and JP 8:Cheylan) Vol. Remote Sensing and Urban Analysis (eds Jean-Paul Donnay, Mike Barnsley and Paul 9: Longley) Page 29. PARTI INTRODUCTION Page 30.

Research paper thumbnail of The Mutatis Mutandis Project: single detection, multiple userschange detection using high spatial-resolution imagery in Dutch urban areas

Research paper thumbnail of Skeletonization of Laser-Scanned Trees in the 3D Raster Domain

Lecture Notes in Geoinformation and Cartography, 2006

ABSTRACT The paper introduces storage and processing of 3-dimensional point clouds, obtained by t... more ABSTRACT The paper introduces storage and processing of 3-dimensional point clouds, obtained by terrestrial laser scanning, in the 3D raster domain. The objects under consideration are trees in production orchards. The purpose is to automatically identify the structure of such trees in terms of the number of branches, their lengths and their thicknesses. An important step in the process is skeletonization. On the basis of a previously developed methodology, a new skeletonization algorithm is developed, which delivers improved results.

Research paper thumbnail of Automatic Shadow Detection in Urban Very-High-Resolution Images Using Existing 3D Models for Free Training

Remote Sensing

Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images wit... more Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images with rich geometric and spectral information and a high update rate are increasingly applied for the purpose of updating 3D models. Shadow detection is the primary step for image interpretation, as shadow causes radiometric distortions. In addition, shadow itself is valuable geometric information. However, shadows are often complicated and environment-dependent. Supervised learning is considered to perform well in detecting shadows when training samples selected from these images are available. Unfortunately, manual labeling of images is expensive. Existing 3D models have been used to reconstruct shadows to provide free, computer-generated training samples, i.e., samples free from intensive manual labeling. However, accurate shadow reconstruction for large-scene 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, mislabeled samples affect classi...

Research paper thumbnail of Moving Window Segmentation Framework for Point Clouds

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

Research paper thumbnail of Article Evaluating MERIS-Based Aquatic Vegetation Mapping

Research paper thumbnail of Improving Geo-Information Reliability by Centralized Change Detection Management

A consortium called Mutatis Mutandis (MutMut), consisting of three Universities and eight produce... more A consortium called Mutatis Mutandis (MutMut), consisting of three Universities and eight producers and users of geo-information, was established in the Netherlands to streamline change detection on a national level. After preliminary investigations concerning market feasibility, three actions are being taken by MutMut: (1) construction of a centralised, web based repository for collection and exchange of changes detected by various actors, (2) design and implementation of an image based, automated change detection methodology, and (3) widening the scope of the project in a European context. This paper focusses on the second action. Objects in the database are used to formulate hypotheses, for which newer imagery may or may not provide evidence. In the latter case, older imagery is considered as well. If evidence for the hypothesis could still be found there, a possible change is detected. The paper shows preliminary results of a first implementation of this methodology. 1.

Research paper thumbnail of Influences of Vegetation on Laser Altimetry – Analysis and Correction Approaches. The Int

The influences of low vegetation on airborne laser scanning are studied. High vegetation is remov... more The influences of low vegetation on airborne laser scanning are studied. High vegetation is removed by filtering, but low vegetation causes systematic errors in digital terrain models. Many researchers have reported that the measurements are too high. The investigation of influences on the laser range measurement improves the understanding of the technology in use and gives explanations for the observed errors. The possibilities of correcting the data with information on the vegetation type are studied, using ground truth data from terrestrial measurements as reference. An alternative approach using texture measures, which does not require information on the land cover type, is presented. Texture has previously been defined for digital images and its equivalent for point clouds is presented here. 1

Research paper thumbnail of Height Texture of Low Vegetation in Airborne Laser Scanner Data and Its Potential for DTM Correction

The influence of low vegetation on laser scanning and its disturbing effect (a systematic positiv... more The influence of low vegetation on laser scanning and its disturbing effect (a systematic positive height shift) during DTM generation is well recognized. Based on our experience in a previous study of estimating the effect of medium-height vegetation (shrubs and bushes) in rough terrain by using point-cloud based co-occurrence texture methods, we now investigate the effect of lower vegetation, such as grass, in very flat marshland conditions. When vegetation is very low, and thus the effect very small, it appears difficult to separate the systematic shift from random measurement noise. When the shift becomes larger, however, it shows significant correlation with texture measures such as slope texture and standard deviation. These measures are derived from the laser data itself and do not require any additional information.

Research paper thumbnail of Virtual sensors - Synthesizing dynamic crowdsensing data into information on static instances

There has been a steady increase in applications that rely on crowdsensing to gather data for ana... more There has been a steady increase in applications that rely on crowdsensing to gather data for analysis purposes. Crowdsensing enables the use of dynamic sensors to collect data on static objects of interest. However, using dynamic sensors in this way causes a problem. The focus of the collected data is on the position of the sensor, not on the object of interest. This results in difficulties in tracking the object of interest in terms of what part of the data from the dynamic sensor describes the object of interest. To shift the focus from the dynamic sensors to a static object, the virtual sensor is introduced. A virtual sensor enables the grouping of data from different dynamic sensors into a single virtual sensor based on measurement positions. The data from the multiple dynamic sensors can be analyzed to provide information per virtual sensor. The data structure of a visual sensor is close to the SensorThings API data structure, which can be expanded to support virtual sensors b...

Research paper thumbnail of Interpolation between isolines based on the Borgefors distance transform

Itc Journal, 1990

ABSTRACT

Research paper thumbnail of Robust Vehicle Tracking in Video Images Being Taken from a Helicopter

Measuring positions, velocities and accelerations/decelerations of individual vehicles in congest... more Measuring positions, velocities and accelerations/decelerations of individual vehicles in congested traffic with standard traffic monitoring equipment, such as inductive loops, are not feasible. The behavior of drivers in the different traffic situations, as re-quired for microscopic traffic flow models, is still not sufficiently known. Remote sensing and computer vision technology are recently being used to solve this problem. In this study we use video images taken from a helicopter above a fixed point of the highway. We address the problem of tracking the movement of previously detected vehicles through a stabilized video sequence. We combine two approaches, optical flow and matching based tracking, improve them by adding constraints and using scale space. Feature elements, i.e. the corners, lines, regions and outlines of each car, are extracted first. Then, optical-flow is used to find for each pixel in the interior of a car the corresponding pixel in the next image, by insertin...

Research paper thumbnail of Voxel-Based Visibility Analysis for Safety Assessment of Urban Environments

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Sep 23, 2019

Research paper thumbnail of Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Remote Sensing

Many urban applications require building polygons as input. However, manual extraction from point... more Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables t...

Research paper thumbnail of 3D Building Change Detection Between Current VHR Images and Past Lidar Data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Change detection is an essential step to locate the area where an old model should be updated. Wi... more Change detection is an essential step to locate the area where an old model should be updated. With high density and accuracy, LiDAR data is often used to create a 3D city model. However, updating LiDAR data at state or nation level often takes years. Very high resolution (VHR) images with high updating rate is therefore an option for change detection. This paper provides a novel and efficient approach to derive pixel-based building change detection between past LiDAR and new VHR images. The proposed approach aims notably at reducing false alarms of changes near edges. For this purpose, LiDAR data is used to supervise the process of finding stereo pairs and derive the changes directly. This paper proposes to derive three possible heights (so three DSMs) by exploiting planar segments from LiDAR data. Near edges, the up to three possible heights are transformed into discrete disparities. A optimal disparity is selected from a reasonable and computational efficient range centered on th...

Research paper thumbnail of On the performance of remote sensing time series reconstruction methods – A spatial comparison

Remote Sensing of Environment, 2016

Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which ... more Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which describe the temporal and spatial variability of global terrestrial vegetation, are inevitably contaminated by clouds, aerosol, snow and ice cover. In general all these conditions yield negative deviations in the time series of NDVI. Many time series reconstruction models have been developed to eliminate effect of the negative deviations and most of them perform differently in different applications and regions. The Harmonic Analysis (HA), Double logistic (DL), Asymmetric Gaussian (AG), Whittaker smoother (WS) and Savitzky–Golay filter (SG) are five of the most widely used time series reconstruction models owing to their simplicity of implementation or the capability to extract phenological metrics from the time series. The performance of these models varies with the NDVI signal and the noise distribution, however, and until now there is no consensus on which method outperforms all others under all situations. Since the NDVI signal and the noise distribution are highly dependent on regional climate and land cover, the reconstruction performance is expected to be spatially variable. Thus this study compared the five reconstruction models at pixel scale to provide practical and biome – specific recommendations for future time series reconstruction applications. Specifically, the 14 years raw daily reflectance data and ancillary Quality Assessment (QA) information from the MODIS sensor were used to generate pixel reference series and noisy series. Then the five candidate models were applied to both reference series and noisy series and three reconstruction performance metrics i.e. Overall Reconstruction Error (ORE), Fitting Related Error (FRE), and Normalized Noise Related Error (NNRE), were calculated. Finally, the performance of the five candidate reconstruction models was evaluated by applying the three metrics. The preliminary results showed that when considering ORE only, the Asymmetric Gaussian model outperforms other models over most areas of high latitude boreal region, while the Savitzky–Golay model gives the best reconstruction performance in tropical and subtropical regions. The FRE and the NNRE helped to reveal the main error sources in the reconstruction in different regions. The comparison method developed and applied in this study led to suggest adaptive selection of the best reconstruction model for specific NDVI signals and noise distribution.

Research paper thumbnail of Recent developments in the ILWIS software kernel

Research paper thumbnail of Principles of remote sensing

Research paper thumbnail of Roof Plane Extraction in Gridded Digital Surface Models

With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to t... more With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to the (application dependent) requirements, robustness, efficiency and automation of the modeling process are becoming more important than achieving the highest possible accuracy and modeling detail from the available LIDAR data. Therefore we opt for development of a 2D grid based LIDAR data analysis approach. An important step is detection and parameterization of planar surfaces (roof elements). The paper reviews four methods, based on analysis of gradients, principal components, least squares and hough transforms, respectively. It introduces a series of improvements to the standard usage of each of those methods and shows results from synthetic and real data. 1.

Research paper thumbnail of Camera-Projector 3D Scanning of a Semi-Submerged Terrain in a Flume

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013

Research paper thumbnail of Remote Sensing and Urban Analysis: GISDATA 9

... Life and Motion of Socio-economic Units (eds Andrew Frank, Jonathan Raper and JP 8:Cheylan) V... more ... Life and Motion of Socio-economic Units (eds Andrew Frank, Jonathan Raper and JP 8:Cheylan) Vol. Remote Sensing and Urban Analysis (eds Jean-Paul Donnay, Mike Barnsley and Paul 9: Longley) Page 29. PARTI INTRODUCTION Page 30.

Research paper thumbnail of The Mutatis Mutandis Project: single detection, multiple userschange detection using high spatial-resolution imagery in Dutch urban areas

Research paper thumbnail of Skeletonization of Laser-Scanned Trees in the 3D Raster Domain

Lecture Notes in Geoinformation and Cartography, 2006

ABSTRACT The paper introduces storage and processing of 3-dimensional point clouds, obtained by t... more ABSTRACT The paper introduces storage and processing of 3-dimensional point clouds, obtained by terrestrial laser scanning, in the 3D raster domain. The objects under consideration are trees in production orchards. The purpose is to automatically identify the structure of such trees in terms of the number of branches, their lengths and their thicknesses. An important step in the process is skeletonization. On the basis of a previously developed methodology, a new skeletonization algorithm is developed, which delivers improved results.

Research paper thumbnail of Automatic Shadow Detection in Urban Very-High-Resolution Images Using Existing 3D Models for Free Training

Remote Sensing

Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images wit... more Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images with rich geometric and spectral information and a high update rate are increasingly applied for the purpose of updating 3D models. Shadow detection is the primary step for image interpretation, as shadow causes radiometric distortions. In addition, shadow itself is valuable geometric information. However, shadows are often complicated and environment-dependent. Supervised learning is considered to perform well in detecting shadows when training samples selected from these images are available. Unfortunately, manual labeling of images is expensive. Existing 3D models have been used to reconstruct shadows to provide free, computer-generated training samples, i.e., samples free from intensive manual labeling. However, accurate shadow reconstruction for large-scene 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, mislabeled samples affect classi...

Research paper thumbnail of Moving Window Segmentation Framework for Point Clouds

ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences