Estimation of impervious surfaces of Beijing,China, with spectral normalized images using linear spectral mixture analysis and artificial neural network |
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| Authors: | Xuefei Hu |
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| Institution: | Department of Geography , Geology, and Anthropology, Indiana State University , 159 science Building, Terre Haute, Indiana, 47809, USA |
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| Abstract: | The objective of this article is to evaluate the effectiveness of various algorithms for estimating impervious surfaces. Linear spectral mixture analysis (LSMA) and multi-layer perceptron (MLP) network using original and spectral normalized images were applied to two ASTER images acquired on 31 August and 9 April 2004, respectively. Accuracy assessment was performed with a Quickbird image. Root-mean-square errors (RMSEs) were calculated and compared. Results indicated that LSMA with original images provided the poorest results. RMSE was 14.8% for the August image and 22.4% for the April image. Results from LSMA with normalized images improved significantly with RMSE of 12.6% for the August image and 18.9% for the April image. The MLP modelling with original images generated slightly better results with RMSE of 12.2% and 18.4% for each image. The MLP modelling of normalized images provided the best estimation, yielding a RMSE of 12.1% for the August image and 18.2% for the April image. |
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| Keywords: | impervious surfaces linear spectral mixture analysis multi-layer perceptron network ASTER images land cover types |
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