LAUSR

Polarimetric synthetic aperture radar (PolSAR) image classification is an essential part of PolSAR image interpretation. In recent years, convolutional neural networks (CNNs) have made significant advances in PolSAR image classification. However, the current CNN-based methods ignore complementary information among different feature maps and correlations between elements of coherence matrix, which can provide discriminative information for classification. Besides, the phase information contained in the complex-valued (CV) coherence matrix cannot be extracted effectively. In this letter, a stacked CV convolutional long short-term memory (ConvLSTM) network called CV-ConvLSTM is proposed for PolSAR classification. Compared to existing methods, CV-ConvLSTM can extract complementary information among different feature maps and utilize the dependencies of elements in the coherency matrix, which can improve the performance of classification. In addition, the CV operations are added to the network, in which phase information is used for better classification. The experimental results of two widely used PolSAR datasets demonstrate that CV-ConvLSTM can obtain superior performance compared with existing CNN methods.