Polsar Image Classification based on Optimal Feature and Convolution Neural Network
Ping Han, Zetao Chen, Yishuang Wan, Zheng Cheng · 2020
This paper proposes a new polarimetric synthetic aperture radar (PolSAR) image classification method which uses optimal feature selection and convolutional neural network (CNN). Firstly, original image features are extracted by performing target decomposition on PolSAR data. Then, in order to reduce feature dimension and improve image classification accuracy, a group of optimal and high correlation property features are selected with the method of filtering and wrappering from the original one. Finally, the optimized CNN model is designed to implement PolSAR image classification. Experiments with measured data show that the algorithm can choose a more effective polarization feature set and achieve better classification results.