Land cover classification of PolSAR image using tensor representation and learning

We propose a tensor representation for polarimetric synthetic aperture radar data and extend the usage of tensor learning technique for feature dimension reduction (DR) in image classification. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multiple polarimetric features and incorporating neighborhood spatial information together. A set of training tensors are determined according to the prior knowledge of the ground truth. Then a tensor learning technique, i.e., multilinear principal component analysis, is applied on the training tensors set to find a tensor subspace that captures most of the variation in the original tensor objects. This process serves as a feature DR step, which is critical for improving the subsequent classification accuracy. Further, the projected tensor samples after DR are fed to the k-nearest neighbor classifier for supervised classification. The performance is verified in both simulated and real datasets. The extracted features are more discriminative in the feature space, and the classification accuracy is significantly improved by at least 10% compared with other existing matrix-based methods.