混合像元的非线性分解通常采用神经元网络模型来拟合，普遍缺乏线性分解模型简单明确的物理意义，导致难于了解像元混合的特点及误差分布的模式。为此，提出均方根误差、双变量统计、置信度估计和混合复杂度等可视化方法来评价非线性混合模型分解的结果，直观地表达出影像中像元的分解精度、混合程度以及误差分布模式等，从而理解非线性混合模型分解的某些特点。实验以投影追踪学习网络（PPLN）为例，利用MODIS与ETM+数据，对MODIS混合像元分解进行了可视化分析，一定程度上展现了PPLN分解的某些特点。通过与反向传播神经网络(

Nonlinear unmixing of mixed pixels in remote sensing imagery are commonly conducted with neural networks (NN) models, which, however, lacks physically based interpretation as linear models. This paper the difficulties of knowing patterns of pixel mixture and error distribution to some extent. This paper proposes to use mean square error, bivariate distribution function, confidential error, and synthetically mixture complexity techniques to analyze and evaluate the pixel mixture, which can provide insights in some features of nonlinear mixture models. Experiment with MODIS associated with ETM+ data demonstrates that the visualized method can obtain the decomposition characterization of PPLN model efficiently. In addition, visualized assessment shows that PPLN provides higher accuracy compared with the BP neural network. The overall unmixing error decreases from 0.182 8 to 0.171 7 in terms of RMSE, improved by 6.5%. The experiment also demonstrates that urban and sparse vegetation are the potential occurrence places where pixels are severely mixed.