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Light Detection and Ranging, (LiDAR) presents a series of unique challenges, the foremost of these being object identification. Because of the ease of aerial collection and high range resolution, analysts are often faced with the challenge of sorting through large datasets and making informed decisions across multiple square miles of data. This problem has made automatic target detection in LiDAR a priority. A novel algorithm is proposed with the overall goal of automatic identification of five object classes within aerially collected LiDAR data: ground, buildings, vehicles, vegetation and power lines. The objective is divided into two parts: region segmentation and object classification. The segmentation portion of the algorithm uses a progressive morphological filter to separate the ground points from the object points. Next, the object points are examined and a Normal Octree Region Merging (NORM) segmentation takes place. This segmentation technique, based on surface normal similarities, subdivides the object points into clusters. Next, for each cluster of object points, a Shape-based Eigen Local Feature (SELF) is computed. Finally, the features are used as the input to a cascade of classifiers, where four individual support vector machines (SVM) are trained to distinguish the object points into the remaining four classes.The ability of the algorithm to segment points into complete objects and also classify each point into its correct class is evaluated. Both the segmentation and classification results are compared to datasets which have been manually ground-truthed. The evaluation demonstrates the success of the proposed algorithm in segmenting and distinguishing between five classes of objects in a LiDAR point cloud. Future work in this direction includes developing a method to identify the volume changes in a scene over time in an effort to provide further contextual information about a given area.
This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).
The aim of the research in this thesis is to investigate and develop machine learning classification and recognition techniques for application in the field of 3D data analysis. The research focuses on three topics using two different types of data: 3D force touch panel display data for user touch behaviour classification, Light Detection and Ranging (LiDAR) 3D point cloud data for sphere recognition and for complex 3D object segmentation and recognition. In the research on 3D force touch detection data on piezoelectric-based interactive panel displays, a nested structured deep neural network is proposed to provide customised touch behaviour analysis. This is a new application of deep learning technology in piezoelectric-based 3D force sensing. In the research on sphere recognition, artificial neural network-based and Range-based novel recognition algorithms are proposed. The Range method is proved to be time efficient and memory saving compared to conventional methods, whereas the ANN-based method is found not to be optimal for dealing with the sphere recognition problem as this problem can be easily simplified into pure mathematical models. In the research on 3D object segmentation and recognition, an automated approach that takes the raw LiDAR scanning data as input and outputs the recognised 3D objects is proposed. This method consists of the following three steps: plane detection, object segmentation, and object recognition. In order to reduce the processing time and the computational complexity of the algorithm to make it possible to run in a reasonable time on an individual processing device with limited memory and computing resources, a novel plane detection approach is proposed. A fast voxel grid-base region growing method is applied to object segmentation. Novel convolutional neural network architectures that take volumetric representation of 3D objects with small voxel grid sizes as inputs are also implemented in the system.
This book is the most comprehensive documentation of the scientific and methodological advances that have taken place in understanding remote sensing data, methods, and applications over last 50 years. In a very practical way it demonstrates the experience, utility, methods and models used in studying a wide array of water applications. There are more than 100 leading global experts in the field contributing to this work.
Spatial technologies like GIS, CAD, and spatial DBMS have proved their applicability and usability in almost every sector of urban development. Urban Planning Systems, Public Participation Systems, and others have been continuously developed and improved contributing to better decision making, communicating ideas between different actors as well as
Compared with traditional remote sensing technologies, airborne Lidar data can provide researchers with additional 3D positional information, which is a key factor for advanced urban research, and particularly that of urban landscape ecology. Therefore, the need for applying Lidar data to a variety of disciplines is rapidly growing. However, the lack of remote sensing background makes the wider use of Lidar data highly difficult for scholars from other disciplines. In contrast to the majority of Lidar-related books that focus on sophisticated principles and general applications of Lidar data, this book provides the reader with a feasible framework for applying airborne Lidar data to urban research. In addition to providing a general introduction to the subject, this book explains in detail a series of case studies to demonstrate how these theoretical models can be employed to address practical urban issues. As such, this book not only provides Lidar scholars with a series of specifically designed research methods, but will also serve to inspire scholars from other disciplines, such as geographers, urban planners, ecologists, and decision-makers, with a complete framework of potential application fields.
This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Image Analysis and Recognition, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The 92 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in topical sections on biometrics: behavioral; biometrics: physiological; classification and regression; object recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image analysis; tracking; medical imaging: image segmentation, image registration, image analysis, coronary image analysis, retinal image analysis, computer aided diagnosis, brain image analysis; cell image analysis; RGB-D camera applications; methods of moments; applications.
Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.
Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for