电子测量与仪器学报

潘伟豪,徐赛博,郭弘扬,万宇坤,吴涛,王超.基于 D-S 证据理论的高分遥感影像建筑物变化检测[J].电子测量与仪器学报,2022,36(8):194-203

基于 D-S 证据理论的高分遥感影像建筑物变化检测

Building change detection of high-resolution remote sensingimages based on D-S evidence theory

DOI：

英文关键词:high-resolution remote sensing image object-oriented evidence theory change detection

基金项目:江苏省博士后基金（2021K013A）、江苏省六大人才高峰工程（2019XYDXX135）、江苏省研究生实践创新计划（2022 132,2022 335）项目资助

作者	单位
潘伟豪	1. 南京信息工程大学长望学院
徐赛博	1. 南京信息工程大学长望学院
郭弘扬	2. 南京信息工程大学电子与信息工程学院
万宇坤	2. 南京信息工程大学电子与信息工程学院
吴涛	2. 南京信息工程大学电子与信息工程学院
王超	1. 南京信息工程大学长望学院，2. 南京信息工程大学电子与信息工程学院

Author	Institution
Pan Weihao	1. Changwang School of Honors, Nanjing University of Information Science & Technology
Xu Saibo	1. Changwang School of Honors, Nanjing University of Information Science & Technology
Guo Hongyang	2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology
Wan Yukun	2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology
Wu Tao	2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology
Wang Chao	1. Changwang School of Honors, Nanjing University of Information Science & Technology,2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology

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中文摘要:

面向城市发展过程中建筑物多样的变化类型,提出了一种基于 D-S 证据理论高分遥感建筑物变化检测方法。基于影像多尺度分割结果,综合多重因素首先设计了一种非建筑物指标 NBI。在此基础上,结合多时相的 NBI、传统建筑物指数 MBI 以及差分特征,构建建筑物变化证据集合。最后,提出了一种结合阴影检测的证据置信度指标,进而构建了一套完整的 D-S 证据理论变化检测模型,从而将建筑物划分为新建、拆除以及改建类。不同地区影像的实验结果表明,所提出模型的变化检测精度和 Kappa 系数分别可达 80%和 0. 7 以上,且在目视分析和定量评价中均优于对比方法。

英文摘要:

Aiming at the variety of building change types in the process of urban development, this paper proposed a high-resolution remote sensing building change detection method based on D-S evidence theory. Based on the results of multi-scale image segmentation, a non-building index NBI is designed by combining multiple factors at first. On this basis, the multi-temporal NBI, traditional building index MBI and differential features are combined to construct the building change evidence set. Finally, an evidence confidence index combined with shadow detection is proposed, then a complete set of D-S evidence theory change detection model is constructed. Thus, the buildings can be divided into new, demolished and rebuilt categories. The experimental results of images from different regions show that the change detection accuracy and Kappa coefficient of the proposed model can reach more than 80% and above 0. 7 respectively, which are better than contrast method in both visual analysis and quantitative evaluation.

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