针对单模型评价图像质量容易产生过拟合的问题,提出基于多模型融合的高光谱图像质量评价算法。以图像噪声、模糊度和云含量为降质特征,建立遥感图像主观评价库,分别选用支持向量回归方法和集成决策树方法对带有评价值的训练集图像建立质量评价单模型。将两个单模型评价结果线性回归拟合,得到模型融合的图像质量评价结果。同时,以广义回归神经网络模型作为参照,分别从均方误差、回归拟合指标、分类准确率、训练时间4个方面对几种模型进行对比。实验结果表明,所提模型融合算法具有较高的拟合精度、较强的泛化能力,并且所需的训练时间相对较少。

In order to solve the problem that image quality is easily overfitted by a single model, a hyperspectral image quality evaluation algorithm is proposed based on multi-model fusion. Taking image noise, ambiguity and cloud content as the degraded features, a remote sensing image subjective evaluation database is established. The support vector regression method and the integrated decision tree method are respectively selected to establish a quality evaluation model for training set images with evaluation values. The image quality evaluation results based on model fusion are obtained via linear regression fitting of the two single model evaluation results. At the same time, the generalized regression neural network model is introduced as a reference, and several models are compared from four aspects of mean square error, regression fitting index, classification accuracy and training time. The experimental results show that the proposed model fusion algorithm has relatively high fitting accuracy, relatively strong generalization ability and relatively little training time.