Exploring Convolutional Lstm for Polsar Image Classification

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important applications in Pol-SAR image processing. More and more deep learning methods are applied to PolSAR image classification. As we know, the polarimetric response of a target is related to the orientation of the target, but the features in rotation domain are not fully used in deep learning. We use a convolutional LSTM (ConvLSTM) along with a sequence of polarization coherent matrices in rotation domain for PolSAR image classification. First, nine different polarization orientation angles (POA) are used to generate nine polarization coherent matrices in rotation domain. Second, a deep learning model that stacked with multiple ConvLSTM layers and fully connected layers is proposed for classification. Finally, the sequence of polarization coherent matrices is fed into the ConvLSTM to classify Pol-SAR images. Experiments show that the classification results of ConvLSTM are better than the LeNet-5.