Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model

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• 作者：Li-gang Ma (1) r> Jin-song Deng (1) (2) r> Huai Yang (1) r> Yang Hong (2) (3) r> Ke Wang (1) r>r>1. Institute of Applied Remote Sensing & Information Technology ; Zhejiang University ; Hangzhou ; 310058 ; China r> 2. School of Civil Engineering and Environmental Sciences and School of Meteorology ; University of Oklahoma ; Norman ; OK ; 73019 ; USA r> 3. State Key Laboratory of Hydroscience and Engineering ; Department of Hydraulic Engineering ; Tsinghua University ; Beijing ; 100084 ; China r>
• 关键词：ZY ; 1 02C satellite ; Classification ; Urban ; Multi ; variable model ; TP751.1
• 刊名：Journal of Zhejiang University - Science C
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：16
• 期：3
• 页码：238-248
• 全文大小：1,114 KB
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• 刊物类别：Computer Science
• 刊物主题：Computer Science, generalr>
• 出版者：Zhejiang University Press, co-published with Springer
• ISSN：1869-196X

文摘

The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite (three bands), and the complexity and heterogeneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi-variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorrelation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy assessment which adopts traditional error matrices with stratified random samples and polygon area consistency (PAC) indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.