Photogrammetric Engineering and Remote Sensing, Feb 1, 2013
Lidar systems have been proven as a cost-effective tool for the collection of high density and ac... more Lidar systems have been proven as a cost-effective tool for the collection of high density and accurate point cloud over physical surfaces. The collected point cloud does not exhibit homogenous point distribution due to the characteristics of the scanning system and/or the physical properties of the scanned surfaces. In order to effectively process the lidar point clouds, local point density variations should be quantified and taken into account for the definition of processing parameters. In this paper, new approaches are presented for the estimation of local point density indices while considering the 3D relationship among lidar points, the physical properties of the reflecting surfaces, and the noise level in the datasets collected by different laser scanners. The impact of considering the estimated local point density variations on the quality of lidar data segmentation results is then investigated by performing a quality control procedure. Quantitative evaluation of segmentation results highlights the efficacy of utilizing the estimated local point density indices for the derivation of more accurate segmentation.
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significant... more Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields' images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%.
Light detection and ranging (LiDAR) systems onboard static and mobile platforms have emerged as a... more Light detection and ranging (LiDAR) systems onboard static and mobile platforms have emerged as a prominent tool for the direct acquisition of accurate point clouds along object surfaces with high density. The widespread adoption of LiDAR systems is propelled by recent advances in laser ranging and scanning technologies as well as direct geo-referencing systems (i.e., integrated global navigation satellite systems and inertial navigation systems—GNSS/INS) which are capable of providing accurate position and orientation of the platform at high frequency. Coupled with advances in the sensor technology, recent development of nontraditional platforms (e.g., unmanned autonomous systems—UAS) and increasing application domains are among the factors leading to unprecedented interest in LiDAR systems.
Over the past few years, laser scanning has been established as a leading technology for the acqu... more Over the past few years, laser scanning has been established as a leading technology for the acquisition of high density 3D spatial information. Digital Terrain Models (DTMs), which can be used for different engineering applications, are obtained by classification of laser data and removing the points that do not belong to terrain surface. The commonly used methods for the classification of laser scanning data are point-based. The major drawback of these methods is focusing on the discontinuities between neighbouring points regardless of the nature of the objects they belong to, which might lead to unreliable classification results. A segmentation-based approach for the classification of both airborne and terrestrial point clouds is presented in this paper. This approach is designed to overcome the drawbacks of point-based classification methods. As the first step, the laser point cloud is segmented by clustering the points with common attributes. To compute precise attributes, an a...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for... more In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction usi...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicle... more The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicles (UAVs) as a platform to collect geospatial data for rapid response applications, especially in hard-to-access and hazardous areas. The UAVs are low-cost mapping vehicles, and they are easy to handle and deploy in-field. These characteristics make UAVs ideal candidates for rapid-response and disaster mitigation scenarios. The majority of the available UAV systems are not capable of real-time/near real-time data processing. This paper introduces a low-cost UAV-based multi-sensor mapping payload which supports real-time processing and can be effectively used in rapid-response applications. The paper introduces the main components of the system, and provides an overview of the proposed payload architecture. Then, it introduces the implementation details of the major building blocks of the system. Finally, the paper presents our conclusions and the future work, in order to achieve real-time...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
In recent years, the increasing incidence of climate-related disasters has tremendously affected ... more In recent years, the increasing incidence of climate-related disasters has tremendously affected our environment. In order to effectively manage and reduce dramatic impacts of such events, the development of timely disaster management plans is essential. Since these disasters are spatial phenomena, timely provision of geospatial information is crucial for effective development of response and management plans. Due to inaccessibility of the affected areas and limited budget of first-responders, timely acquisition of the required geospatial data for these applications is usually possible only using low-cost imaging and georefencing sensors mounted on unmanned platforms. Despite rapid collection of the required data using these systems, available processing techniques are not yet capable of delivering geospatial information to responders and decision makers in a timely manner. To address this issue, this paper introduces a new technique for dense 3D reconstruction of the affected scenes which can deliver and improve the needed geospatial information incrementally. This approach is implemented based on prior 3D knowledge of the scene and employs computationally-efficient 2D triangulation, feature descriptor, feature matching and point verification techniques to optimize and speed up 3D dense scene reconstruction procedure. To verify the feasibility and computational efficiency of the proposed approach, an experiment using a set of consecutive images collected onboard a UAV platform and prior low-density airborne laser scanning over the same area is conducted and step by step results are provided. A comparative analysis of the proposed approach and an available image-based dense reconstruction technique is also conducted to prove the computational efficiency and competency of this technique for delivering geospatial information with pre-specified accuracy.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Accurate 3D surface reconstruction of our environment has become essential for an unlimited numbe... more Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniqu...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
In the last few years, low-cost UAV systems have been acknowledged as an affordable technology fo... more In the last few years, low-cost UAV systems have been acknowledged as an affordable technology for geospatial data acquisition that can meet the needs of a variety of traditional and non-traditional mapping applications. In spite of its proven potential, UAV-based mapping is still lacking in terms of what is needed for it to become an acceptable mapping tool. In other words, a well-designed system architecture that considers payload restrictions as well as the specifications of the utilized direct geo-referencing component and the imaging systems in light of the required mapping accuracy and intended application is still required. Moreover, efficient data processing workflows, which are capable of delivering the mapping products with the specified quality while considering the synergistic characteristics of the sensors onboard, the wide range of potential users who might lack deep knowledge in mapping activities, and time constraints of emerging applications, are still needed to be ...
Automatic building extraction remains an open research area in digital photogrammetry. While many... more Automatic building extraction remains an open research area in digital photogrammetry. While many algorithms have been proposed for building extraction, none of them solve the problem completely. This paper proposes a system for increasing the degree of automation in extraction of building features with different rooftops from high resolution Multispectral satellite images (e.g., IKONOS and Quickbird) in Middle East countries. Following on, the implementation and functionality of software developed on the basis of neural networks approach are also explained. As known, neural networks have capabilities as pattern recognition and object extraction from remotely sensed data. The software has been designed and developed in C# programming environment and it is rather user friendly due to the fact that little knowledge is required for the users about neural networks theory. The proposed system works in two different phases: the first phase is learning, and the second phase is application....
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significant... more Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was import...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
In recent years, laser scanning systems have been recognized as a fast and accurate technology fo... more In recent years, laser scanning systems have been recognized as a fast and accurate technology for the acquisition of high density spatial data. The advent of these systems has reduced the cost and increased the availability of accurate 3D data for mapping, modelling, and monitoring applications. The original laser scanning data does not explicitly provide meaningful information about the characteristics of the scanned surfaces. Therefore, reliable processing procedures are applied for information extraction from these datasets. However, the derived surfaces through laser scanning data processing cannot be effectively interpreted due to the lack of spectral information. To resolve this problem, a new texturing procedure is introduced in this paper to improve the interpretability of laser scanning-derived surfaces using spectral information from overlapping imagery. In this texturing approach, individual planar regions, derived through a laser scanning data segmentation procedure, ar...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Automatic processing and object extraction from 3D laser point cloud is one of the major research... more Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Over the past few years, LiDAR systems have been established as a leading technology for the acqu... more Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification). Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for precise LiDAR data processing.
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Laser scanning systems have been widely adopted for directly providing 3D point cloud over physic... more Laser scanning systems have been widely adopted for directly providing 3D point cloud over physical surfaces at high density. However, the collected point cloud should undergo manipulation approaches to be utilized for diverse civil, industrial, and military applications. Different processing techniques have consequently been implemented for the extraction of low-level features from this data. Linear/cylindrical features are among the most important primitives that could be extracted from laser scanning data, especially those collected in industrial sites and urban areas. This paper presents a novel approach for the identification, parameterization, and segmentation of these features in a laser point cloud. In the first step of the proposed approach, the points which belong to linear/cylindrical features are detected and their appropriate representation models are chosen based on the principal component analysis of their local neighborhood. The approximate direction and position parameters of the identified linear/cylindrical features are then refined using an iterative line/cylinder fitting procedure. A parameter-domain segmentation method is finally applied to isolate the points which belong to individual linear/cylindrical features in direction and position attribute spaces, respectively. Experimental results from real datasets will demonstrate the feasibility of the proposed approach for the extraction of linear/cylindrical features from laser scanning data.
Wearable electronic devices have experienced increasing development with the advances in the semi... more Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments.
Photogrammetric Engineering and Remote Sensing, Feb 1, 2013
Lidar systems have been proven as a cost-effective tool for the collection of high density and ac... more Lidar systems have been proven as a cost-effective tool for the collection of high density and accurate point cloud over physical surfaces. The collected point cloud does not exhibit homogenous point distribution due to the characteristics of the scanning system and/or the physical properties of the scanned surfaces. In order to effectively process the lidar point clouds, local point density variations should be quantified and taken into account for the definition of processing parameters. In this paper, new approaches are presented for the estimation of local point density indices while considering the 3D relationship among lidar points, the physical properties of the reflecting surfaces, and the noise level in the datasets collected by different laser scanners. The impact of considering the estimated local point density variations on the quality of lidar data segmentation results is then investigated by performing a quality control procedure. Quantitative evaluation of segmentation results highlights the efficacy of utilizing the estimated local point density indices for the derivation of more accurate segmentation.
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significant... more Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields' images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%.
Light detection and ranging (LiDAR) systems onboard static and mobile platforms have emerged as a... more Light detection and ranging (LiDAR) systems onboard static and mobile platforms have emerged as a prominent tool for the direct acquisition of accurate point clouds along object surfaces with high density. The widespread adoption of LiDAR systems is propelled by recent advances in laser ranging and scanning technologies as well as direct geo-referencing systems (i.e., integrated global navigation satellite systems and inertial navigation systems—GNSS/INS) which are capable of providing accurate position and orientation of the platform at high frequency. Coupled with advances in the sensor technology, recent development of nontraditional platforms (e.g., unmanned autonomous systems—UAS) and increasing application domains are among the factors leading to unprecedented interest in LiDAR systems.
Over the past few years, laser scanning has been established as a leading technology for the acqu... more Over the past few years, laser scanning has been established as a leading technology for the acquisition of high density 3D spatial information. Digital Terrain Models (DTMs), which can be used for different engineering applications, are obtained by classification of laser data and removing the points that do not belong to terrain surface. The commonly used methods for the classification of laser scanning data are point-based. The major drawback of these methods is focusing on the discontinuities between neighbouring points regardless of the nature of the objects they belong to, which might lead to unreliable classification results. A segmentation-based approach for the classification of both airborne and terrestrial point clouds is presented in this paper. This approach is designed to overcome the drawbacks of point-based classification methods. As the first step, the laser point cloud is segmented by clustering the points with common attributes. To compute precise attributes, an a...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for... more In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction usi...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicle... more The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicles (UAVs) as a platform to collect geospatial data for rapid response applications, especially in hard-to-access and hazardous areas. The UAVs are low-cost mapping vehicles, and they are easy to handle and deploy in-field. These characteristics make UAVs ideal candidates for rapid-response and disaster mitigation scenarios. The majority of the available UAV systems are not capable of real-time/near real-time data processing. This paper introduces a low-cost UAV-based multi-sensor mapping payload which supports real-time processing and can be effectively used in rapid-response applications. The paper introduces the main components of the system, and provides an overview of the proposed payload architecture. Then, it introduces the implementation details of the major building blocks of the system. Finally, the paper presents our conclusions and the future work, in order to achieve real-time...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
In recent years, the increasing incidence of climate-related disasters has tremendously affected ... more In recent years, the increasing incidence of climate-related disasters has tremendously affected our environment. In order to effectively manage and reduce dramatic impacts of such events, the development of timely disaster management plans is essential. Since these disasters are spatial phenomena, timely provision of geospatial information is crucial for effective development of response and management plans. Due to inaccessibility of the affected areas and limited budget of first-responders, timely acquisition of the required geospatial data for these applications is usually possible only using low-cost imaging and georefencing sensors mounted on unmanned platforms. Despite rapid collection of the required data using these systems, available processing techniques are not yet capable of delivering geospatial information to responders and decision makers in a timely manner. To address this issue, this paper introduces a new technique for dense 3D reconstruction of the affected scenes which can deliver and improve the needed geospatial information incrementally. This approach is implemented based on prior 3D knowledge of the scene and employs computationally-efficient 2D triangulation, feature descriptor, feature matching and point verification techniques to optimize and speed up 3D dense scene reconstruction procedure. To verify the feasibility and computational efficiency of the proposed approach, an experiment using a set of consecutive images collected onboard a UAV platform and prior low-density airborne laser scanning over the same area is conducted and step by step results are provided. A comparative analysis of the proposed approach and an available image-based dense reconstruction technique is also conducted to prove the computational efficiency and competency of this technique for delivering geospatial information with pre-specified accuracy.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Accurate 3D surface reconstruction of our environment has become essential for an unlimited numbe... more Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniqu...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
In the last few years, low-cost UAV systems have been acknowledged as an affordable technology fo... more In the last few years, low-cost UAV systems have been acknowledged as an affordable technology for geospatial data acquisition that can meet the needs of a variety of traditional and non-traditional mapping applications. In spite of its proven potential, UAV-based mapping is still lacking in terms of what is needed for it to become an acceptable mapping tool. In other words, a well-designed system architecture that considers payload restrictions as well as the specifications of the utilized direct geo-referencing component and the imaging systems in light of the required mapping accuracy and intended application is still required. Moreover, efficient data processing workflows, which are capable of delivering the mapping products with the specified quality while considering the synergistic characteristics of the sensors onboard, the wide range of potential users who might lack deep knowledge in mapping activities, and time constraints of emerging applications, are still needed to be ...
Automatic building extraction remains an open research area in digital photogrammetry. While many... more Automatic building extraction remains an open research area in digital photogrammetry. While many algorithms have been proposed for building extraction, none of them solve the problem completely. This paper proposes a system for increasing the degree of automation in extraction of building features with different rooftops from high resolution Multispectral satellite images (e.g., IKONOS and Quickbird) in Middle East countries. Following on, the implementation and functionality of software developed on the basis of neural networks approach are also explained. As known, neural networks have capabilities as pattern recognition and object extraction from remotely sensed data. The software has been designed and developed in C# programming environment and it is rather user friendly due to the fact that little knowledge is required for the users about neural networks theory. The proposed system works in two different phases: the first phase is learning, and the second phase is application....
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significant... more Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was import...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
In recent years, laser scanning systems have been recognized as a fast and accurate technology fo... more In recent years, laser scanning systems have been recognized as a fast and accurate technology for the acquisition of high density spatial data. The advent of these systems has reduced the cost and increased the availability of accurate 3D data for mapping, modelling, and monitoring applications. The original laser scanning data does not explicitly provide meaningful information about the characteristics of the scanned surfaces. Therefore, reliable processing procedures are applied for information extraction from these datasets. However, the derived surfaces through laser scanning data processing cannot be effectively interpreted due to the lack of spectral information. To resolve this problem, a new texturing procedure is introduced in this paper to improve the interpretability of laser scanning-derived surfaces using spectral information from overlapping imagery. In this texturing approach, individual planar regions, derived through a laser scanning data segmentation procedure, ar...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Automatic processing and object extraction from 3D laser point cloud is one of the major research... more Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Over the past few years, LiDAR systems have been established as a leading technology for the acqu... more Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification). Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for precise LiDAR data processing.
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Laser scanning systems have been widely adopted for directly providing 3D point cloud over physic... more Laser scanning systems have been widely adopted for directly providing 3D point cloud over physical surfaces at high density. However, the collected point cloud should undergo manipulation approaches to be utilized for diverse civil, industrial, and military applications. Different processing techniques have consequently been implemented for the extraction of low-level features from this data. Linear/cylindrical features are among the most important primitives that could be extracted from laser scanning data, especially those collected in industrial sites and urban areas. This paper presents a novel approach for the identification, parameterization, and segmentation of these features in a laser point cloud. In the first step of the proposed approach, the points which belong to linear/cylindrical features are detected and their appropriate representation models are chosen based on the principal component analysis of their local neighborhood. The approximate direction and position parameters of the identified linear/cylindrical features are then refined using an iterative line/cylinder fitting procedure. A parameter-domain segmentation method is finally applied to isolate the points which belong to individual linear/cylindrical features in direction and position attribute spaces, respectively. Experimental results from real datasets will demonstrate the feasibility of the proposed approach for the extraction of linear/cylindrical features from laser scanning data.
Wearable electronic devices have experienced increasing development with the advances in the semi... more Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments.
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Papers by Zahra Lari