Multi-type change detection of building models by integrating spatial and spectral information

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101044

Title:	Multi-type change detection of building models by integrating spatial and spectral information
Authors:	黃智遠;Chen, Liang-Chien;Huang, Chih-Yuan;Teo, Tee-Ann
Contributors:	太空及遙測研究中心
Keywords:	Animal, plant and microbial ecology;Applied geophysics;Biological and medical sciences;buildings;Earth sciences;Earth, ocean, space;Exact sciences and technology;Fundamental and applied biological sciences. Psychology;General aspects. Techniques;Internal geophysics;land surveying;lidar;surveys;Teledetection and vegetation maps
Date:	2012-01-01
Issue Date:	2026-04-21 14:22:03 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Abingdon: Taylor & Francis
Abstract:	摘要： Detecting building changes followed by updates is preferable for efficient revisions to building models. Additionally, more change types can be detected with spatial information provided by building models for reducing land surveying work. Therefore, for efficient building of model revision and land surveys, this work applies a new multi-type change detection scheme with new light detection and ranging (LIDAR) point clouds, new aerial images and existing building models. By integrating the spatial information from LIDAR data and image-based spectral information, this work identifies changes to existing buildings and identifies newly built and changed buildings. To provide an initial value for further revisions, new building regions are generated from change detection results. Experimental results demonstrate that the proposed scheme has high accuracy for both change type determination and building region generation. To provide comprehensive observations, experimental results deemed unreliable are scrutinized. 出版者： Abingdon: Taylor & Francis 出版日期： 2012-03-20 出處： International journal of remote sensing, 2012-03, Vol.33 (6), p.1655-1681 版權： Copyright Taylor & Francis Group, LLC 2012 版權： 2015 INIST-CNRS 識別號： ISSN: 0143-1161 識別號： ISSN: 1366-5901 識別號： EISSN: 1366-5901 識別號： DOI: 10.1080/01431161.2011.593584 識別號： CODEN: IJSEDK
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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