PSONN used for remote-sensing image classification

This section proposes a hybrid classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Early stop (ES) was adopted to prevent overfitting. Finally, a 3-layer neural network (NN) was constructed, and particle swarm optimization (PSO) was employed to fasten the learning. The results on Flevoland sites compared to adaptive back-propagation (ABP) neural network demonstrated the validness and superiority of our method in terms of confusion matrix and overall accuracy.