A Comparative Study of Pattern Recognition Models on the PaviaU Dataset

This report provides a comprehensive study focused on land cover classification and remote sensing image analysis using the PaviaU dataset. The report first introduces the basic characteristics and application background of the data set, and then discusses in detail the application of feature selection and dimensionality reduction techniques, especially linear discriminant analysis (LDA) and principal component analysis (PCA). In terms of model application, the report not only uses traditional machine learning models, such as support vector machines, Bayesian and KNN posterior probabilities, but also explores the performance of these models under different parameter settings. Through a series of experiments, the study found that optimizing feature selection and dimensionality reduction techniques can significantly improve the classification accuracy of the model. Finally, the report compares different models and suggests alternative improvements.