DOI: 10.1016/j.jag.2016.08.010
Scopus记录号: 2-s2.0-85018196280
论文题名: Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment
作者: Leichtle T ; , Geiß C ; , Wurm M ; , Lakes T ; , Taubenböck H
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 54 起始页码: 15
结束页码: 27
语种: 英语
英文关键词: Change detection
; Clustering
; Object-based image analysis
; Principal component analysis
; Urban environment
; Very-high resolution (VHR) remote sensing
Scopus关键词: cluster analysis
; image analysis
; principal component analysis
; remote sensing
; urban area
英文摘要: Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of κ statistics of 0.8–0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to κ statistics, which underlines the robustness of the proposed approach. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79947
Appears in Collections: 气候变化事实与影响
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作者单位: Company for Remote Sensing and Environmental Research (SLU), Kohlsteiner Straße 5, München, Germany; German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Münchner Straße 20, Weßling, Germany; Humboldt-Universität zu Berlin, Geography Department, Rudower Chaussee 16Berlin, Germany
Recommended Citation:
Leichtle T,, Geiß C,, Wurm M,et al. Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,54