针对传统方法在提取城市不透水层中的许多局限性，采用两种非线性光谱混合分解模型，包括混合调谐匹配滤波和多层感知器神经网络，通过混合像元分解获取城市不透水层.混合调谐匹配滤波利用用户选择的端元，通过最大化端元响应并减少未知背景信息的影响，进行局部分解端元.多层感知器由多个感知器组成，能够很好的进行非线性学习.对Landsat TM遥感影像进行最大噪声分离，使其转换到另外一个特征空间.利用新生成数据集的前三个成分(占90%以上信息量)进行纯净像元提取，并利用N维可视化分析器寻找出四个进行分解的端元：植被、高反射率地物、低反射率地物和土壤。不透水层则由高反射率和低反射率两个分量估算而成。对不同模型提取的结果，利用QuickBird多光谱图像评价其准确性.实验结果表明人工神经网络的精度最高，即非线性光谱混合模型同样可以有效地提取不透水层，精度甚至优于线性模型.

Aiming at overcoming the limitations in extracting impervious surface by traditional methods, two non-linear spectral mixture models, Mixture Tuned Matched Filtering (MTMF) and Multi-Layer Perceptron(MLP) neural network, are used to decompose all pixels to the four fraction images representing the abundance of four endmembers. In these models, MTMF performs a “partial” unmixing by only finding the abundance of a single, user-defined endmember, by maximizing the response of the endmember of interest and minimizing the response of the composite unknown background. The MLP is a hierarchical structure of several perceptrons, and capable of learning a rich variety of nonlinear decision surfaces. The Maximum Noise Fraction(MNF) is used to transform the six bands of TM image into a new feature space and the first three components accounting for the majority (more than 90%) of total information content are used to endmember extraction. After that, the Pure Pixel Index(PPI) is used to select pure pixels. The N-dimensional visualizer is used for assisting selection of four endmembers: vegetation, high-albedo objects, low-albedo objects and soil. The fraction images are derived to represent the abundance of the above four endmember. Impervious surface is estimated by analyzing high-albedo and low-albedo fraction images. QuickBird multi-spectral image is used to evaluate the accuracy of impervious surface extraction by different methods. Experimental results indicate that the accuracy of artificial neural network is higher than others, which means non-linear spectral mixture models is also effective to impervious area extraction, even better than linear models.