Meta-Graph Representation Learning for PolSAR Image Classification

Most existing polarimetric synthetic aperture radar (PolSAR) image classification methods are only valid under the assumption of identical imaging platforms and terrain categories for both training and test sets. To overcome this limitation, we propose a meta-graph representation learning (MGRL) method for PolSAR image classification with cross-platform and cross-category implementation. First, an integrated network is developed to learn the global-local representations of PolSAR images, which consists of a trumpet convolutional network (TCN) to learn the local scattering features of pixels and a graph convolutional network (GCN) for modeling the global structure of polarization information. Then, a comprehensive and transferable embedding of pixels is derived by collaborative optimization on multiple meta-learning tasks, which enables MGRL to recognize new classes not seen during training. Thus, the learned transferable representations can be quickly adapted to cross-platform and cross-category tasks with few labeled samples. Extensive experiments on several live airborne and spaceborne PolSAR datasets validate the effectiveness and advantages of MGRL over its counterparts.