A hybrid network for PolSAR image classification based on polarization orientation angles

Abstract

ABSTRACT Polarimetric synthetic aperture radar (PolSAR) image classification is a significant task of PolSAR applications. In recent years, deep learning-based methods using spatial features have achieved satisfactory results in PolSAR classification. However, there are still some important features left to be exploited. One is the target scattering orientation diversity feature that contains rich hidden information. In this letter, a hybrid network is proposed to model the target scattering orientation diversity feature and spatial feature. First, the polarimetric scoherency matrix is expanded to polarization coherency matrix sequence by different polarization orientation angles (POAs). Then, the long short-term memory (LSTM) network and the convolutional neural networks (CNN) are proposed to process the polarization coherency matrix sequence and the square neighbourhood of pixels, respectively. Finally, the features of the two sub-networks are combined to improve the classification accuracy. The experiments demonstrate that the proposed method can obtain superior and robust performance.