Hyperspectral Image Quality Evaluation Based on Multi-Model Fusion

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 arc respectively selected to establish a quality evaluation model for training set images with evaluation values. The image quality evaluation results based on model fusion arc 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 arc 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.