Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment
Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment
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DOI：
10.1016/j.jag.2016.08.010
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发表时间：
2017-02-01
影响因子：
7.5
通讯作者：
Taubenboeck, Hannes
中科院分区：
文献类型：
--
作者：
Leichtle, Tobias;Geiss, Christian;Taubenboeck, Hannes
监测变化是遥感最重要的内在能力之一。可用甚高分辨率(VHR)遥感图像的数量稳步增加，需要高度自动化的方法，因此，变化检测在很大程度上是无人监督的概念。此外，应对这一挑战的新程序应该能够处理由不同传感器获取的遥感数据。因此，特别是在快速变化的复杂城市环境中，VHR数据中存在的高度详细表明采用了基于对象的概念来进行变化检测。针对单个建筑物，提出了一种新的基于对象的无监督变化检测方法。首先，执行主成分分析以及用于确定相关主成分的数量的唯一过程，作为变化检测的前身。其次，将k-均值聚类应用于建筑物变化和变化的区分。以这种方式，评估可从多时相VHR数据中导出的几组基于对象的差异特征关于其用于变化检测的区别性属性。此外，还量化了使用不同传感器采集的VHR数据时观测几何偏差的影响。总体而言，对于不同的特征组，拟议的工作流返回了可行的结果，其kappa统计量的顺序为0.8-0.9或更高，这证明了其适用于动态城市环境中的非监督变化检测。对于来自不同传感器的图像，根据kappa统计量，不同的观察几何形状对变化检测结果的影响仅为0.04量级，这突显了该方法的稳健性。(C)2016爱思唯尔B.V.保留所有权利。
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 kappa 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 kappa statistics, which underlines the robustness of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.