Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment
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- 作者：Tobias Leichtlea ; b ; tobias.leichtle@slu-web.de" class="auth_mail" title="E-mail the corresponding author ; Christian Geiß ; b ; Michael Wurmb ; Tobia Lakesc ; Hannes Taubenbö ; ckb
- 关键词：Change detection ; Object-based image analysis ; Principal component analysis ; Clustering ; Very-high resolution (VHR) remote sensing ; Urban environment
- 刊名：International Journal of Applied Earth Observation and Geoinformation
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：54
- 期：Complete
- 页码：15-27
- 全文大小：5039 K
文摘
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We present a new change detection approach with focus on individual buildings.
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It is capable of handling VHR remote sensing images acquired by different sensors.
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Deviating viewing geometries of VHR data affect the approach only slightly.
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PCA of object-based difference features is followed by k-means clustering.
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Viable and robust results are achieved in the order of κ statistics of 0.8–0.9.
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