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Estimation of impervious surfaces of Beijing, China, with spectral normalized images using linear spectral mixture analysis and artificial neural network

机译：使用线性光谱混合分析和人工神经网络的光谱归一化图像估算北京的不透水面

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摘要

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.
机译：本文的目的是评估各种算法估计不透水表面的有效性。使用原始和光谱归一化图像的线性光谱混合分析（LSMA）和多层感知器（MLP）网络分别应用于2004年8月31日和2004年4月9日获得的两个ASTER图像。使用Quickbird图像进行准确性评估。计算并比较均方根误差（RMSE）。结果表明，具有原始图像的LSMA提供的结果最差。 8月图像的RMSE为14.8％，4月图像的RMSE为22.4％。具有标准化图像的LSMA结果显着改善，8月图像的RMSE为12.6％，4月图像的RMSE为18.9％。使用原始图像的MLP建模产生了更好的结果，每个图像的RMSE为12.2％和18.4％。归一化图像的MLP建模提供了最佳估计，八月图像的RMSE为12.1％，四月图像的RMSE为18.2％。

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