Complex-Valued 3-D Convolutional Neural Network for PolSAR Image Classification

摘要

Recently, convolutional neural network (CNN) has been successfully utilized in the terrain classification of polarimetric synthetic aperture radar (PolSAR) images. However, most CNN-based models are currently limited to handle 2-D real-valued inputs, and therefore, the physical scattering mechanism contained in the complex-valued (CV) covariance/coherency matrix cannot be extracted effectively. For this reason, CV 3-D CNN (CV-3D-CNN) is proposed for PolSAR image classification. Compared with CNN, CV-3D-CNN simultaneously extracts hierarchical features in both the spatial and the scattering dimensions by performing 3-D CV convolutions, thereby capturing the physical property from polarimetric adjacent resolution cells. Experiments on real PolSAR images classification demonstrate the effectiveness and the superiorities of CV-3D-CNN and illustrate that CV-3D-CNN can deal with scattering characteristic in a more complete manner and achieve better performance in PolSAR image classification.