Share Email Print

Journal of Applied Remote Sensing

Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine
Author(s): Zhongchang Sun; Huadong Guo; Xinwu Li; Linlin Lu; Xiaoping Du
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 1 January 2011
PDF: 18 pages
J. Appl. Remote Sens. 5(1) 053501 doi: 10.1117/1.3539767
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Zhongchang Sun, Ctr. for Earth Observation and Digital Earth (China)
Huadong Guo, Ctr. for Earth Observation and Digital Earth (China)
Xinwu Li, Ctr. for Earth Observation and Digital Earth (China)
Linlin Lu, Ctr. for Earth Observation and Digital Earth (China)
Xiaoping Du, Ctr. for Earth Observation and Digital Earth (China)

© SPIE. Terms of Use
Back to Top