Full Text:   <2583>

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CLC number: TP751.1

On-line Access: 2015-03-04

Received: 2014-03-07

Revision Accepted: 2014-05-30

Crosschecked: 2015-02-04

Cited: 2

Clicked: 6980

Citations:  Bibtex RefMan EndNote GB/T7714


Li-gang Ma


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.3 P.238-248


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

Author(s):  Li-gang Ma, Jin-song Deng, Huai Yang, Yang Hong, Ke Wang

Affiliation(s):  Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   11114054@zju.edu.cn, jsong_deng@zju.edu.cn, kwang@zju.edu.cn

Key Words:  ZY-1 02C satellite, Classification, Urban, Multi-variable model

Li-gang Ma, Jin-song Deng, Huai Yang, Yang Hong, Ke Wang. Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 238-248.

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publisher="Zhejiang University Press & Springer",

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%T Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model
%A Li-gang Ma
%A Jin-song Deng
%A Huai Yang
%A Yang Hong
%A Ke Wang
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A1 - Li-gang Ma
A1 - Jin-song Deng
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A1 - Ke Wang
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DOI - 10.1631/FITEE.1400083

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.

This is a useful manuscript comparing the metrics for landscape classification using multi-variables from the relatively new 02C satellite imagery. This is an important independent test for using new data, so that justifies the significance of the work. Overall the paper is clear and straightforward in its approach.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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