Research Papers

Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine

[+] Author Affiliations
Zhongchang Sun

Chinese Academy of Sciences, Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, No. 9 Dengzhuang South Road, Haidian District, Beijing, China 100094

Graduate University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, China 100049

Huadong Guo

Chinese Academy of Sciences, Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, No. 9 Dengzhuang South Road, Haidian District, Beijing, China 100094

Xinwu Li

Chinese Academy of Sciences, Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, No. 9 Dengzhuang South Road, Haidian District, Beijing, China 100094

Linlin Lu

Chinese Academy of Sciences, Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, No. 9 Dengzhuang South Road, Haidian District, Beijing, China 100094

Xiaoping Du

Chinese Academy of Sciences, Laboratory of Digital Earth Science, Center for Earth Observation and Digital Earth, No. 9 Dengzhuang South Road, Haidian District, Beijing, China 100094

Graduate University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, China 100049

J. Appl. Remote Sens. 5(1), 053501 (February 23, 2011). doi:10.1117/1.3539767
History: Received May 28, 2010; Revised December 01, 2010; Accepted December 10, 2010; Published February 23, 2011; Online February 23, 2011
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In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R2 were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R2 of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVM's capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Zhongchang Sun ; Huadong Guo ; Xinwu Li ; Linlin Lu and Xiaoping Du
"Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine", J. Appl. Remote Sens. 5(1), 053501 (February 23, 2011). ; http://dx.doi.org/10.1117/1.3539767


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