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The research objectives aimed to achieve thorough the thesis are to develop methods for reconstructing models of building and PL objects of interest in the power line (PL) corridor area from airborne LiDAR data. For this, it is mainly concerned with the model selection problem for which model is more optimal in representing the given data set. This means that the parametric relations and geometry of object shapes are unknowns and optimally determined by the verification of hypothetical models. Therefore, the proposed method achieves high adaptability to the complex geometric forms of building and PL objects. For the building modeling, the method of implicit geometric regularization is proposed to rectify noisy building outline vectors which are due to noisy data. A cost function for the regularization process is designed based on Minimum Description Length (MDL) theory, which favours smaller deviation between a model and observation as well as orthogonal and parallel properties between polylines. Next, a new approach, called Piecewise Model Growing (PMG), is proposed for 3D PL model reconstruction using a catenary curve model. It piece-wisely grows to capture all PL points of interest and thus produces a full PL 3D model. However, the proposed method is limited to the PL scene complexity, which causes PL modeling errors such as partial, under- and over-modeling errors. To correct the incompletion of PL models, the inner and across span analysis are carried out, which leads to replace erroneous PL segments by precise PL models. The inner span analysis is performed based on the MDL theory to correct under- and over-modeling errors. The across span analysis is subsequently carried out to correct partial-modeling errors by finding start and end positions of PLs which denotes Point Of Attachment (POA). As a result, this thesis addresses not only geometrically describing building and PL objects but also dealing with noisy data which causes the incompletion of models. In the practical aspects, the results of building and PL modeling should be essential to effectively analyze a PL scene and quickly alleviate the potentially hazardous scenarios jeopardizing the PL system.
This book constitutes the refereed proceedings of the First Pacific Rim Symposium on Image and Video Technology, PSIVT 2006, held in Hsinchu, Taiwan in December 2006. The 76 revised full papers and 58 revised poster papers cover a wide range of topics, including all aspects of video and multimedia, both technical and artistic perspectives and both theoretical and practical issues.
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D
This book contains keynote lectures and full papers presented at the International Symposium on Computational Modelling of Objects Represented in Images (CompIMAGE), held in Coimbra, Portugal, on 20-21 October 2006. International contributions from nineteen countries provide a comprehensive coverage of the current state-of-the-art in the fields of: - Image Processing and Analysis; - Image Segmentation; - Data Interpolation; - Registration, Acquisition and Compression; - 3D Reconstruction; - Objects Tracking; - Motion and Deformation Analysis; - Objects Simulation; - Medical Imaging; - Computational Bioimaging and Visualization. Related techniques also covered in this book include the finite element method, modal analyses, stochastic methods, principal and independent components analyses and distribution models. Computational Modelling of Objects Represented in Images will be useful to academics, researchers and professionals in Computational Vision (image processing and analysis), Computer Sciences, and Computational Mechanics.
Despite advances in remote sensing technology and computational power, automatic land use classification has remained a difficult process, and successful approaches require the incorporation of ancillary GIS data. This study seeks to address the issues which have held back land use classifications making exclusive use of remotely sensed data by using buildings as an alternative to parcels for classification and using ontology to identify existing classification problems and delineate avenues for improving classification methodology. LIDAR point cloud data from the City of Austin is used exclusively for the initial building extraction process, and an impervious surface classification layer derived from DOQQ aerial photography is joined with this layer for the building classification process. The building extraction employs segmentation in Definiens Professional and Neural Network classification in MATLAB and achieves a segment level of accuracy of 92% and kappa of 0.7, with an adjusted building detection rate of 74%, after the exclusion of buildings under 50 square meters in area. A land-use classification scheme using classes derived from the City of Austin's Urban Land Use codes is applied to ground truth buildings and results in a classification accuracy of 80.4% with a kappa of 0.35. When this classification methodology is applied to the extracted buildings the result is overall accuracy is 78.6% with a kappa of 0.33. Further avenues for improving these results are suggested by the ontology and an exploration of the errors.
This two volume set (CCIS 398 and 399) constitutes the refereed proceedings of the International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2013, held in Wuhan, China, in November 2013. The 136 papers presented, in addition to 4 keynote speeches and 5 invited sessions, were carefully reviewed and selected from 522 submissions. The papers are divided into 5 sessions: smart city in resource management and sustainable ecosystem, spatial data acquisition through RS and GIS in resource management and sustainable ecosystem, ecological and environmental data processing and management, advanced geospatial model and analysis for understanding ecological and environmental process, applications of geo-informatics in resource management and sustainable ecosystem.