DOI: 10.1016/j.jag.2017.04.002
Scopus记录号: 2-s2.0-85028409107
论文题名: Class imbalance in unsupervised change detection – A diagnostic analysis from urban remote sensing
作者: Leichtle T ; , Geiß C ; , Lakes T ; , Taubenböck H
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 60 起始页码: 83
结束页码: 98
语种: 英语
英文关键词: Change detection
; Class imbalance
; Clustering
; Object-based image analysis (OBIA)
; Urban environment
; Very-high resolution (VHR) remote sensing
Scopus关键词: accuracy assessment
; algorithm
; classification
; detection method
; image analysis
; image resolution
; remote sensing
; sensitivity analysis
; urban area
英文摘要: Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k‐means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79995
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 16, Berlin, Germany
Recommended Citation:
Leichtle T,, Geiß C,, Lakes T,et al. Class imbalance in unsupervised change detection – A diagnostic analysis from urban remote sensing[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,60