Learning task-driven polarimetric target decomposition: A new perspective

Polarimetric target decomposition aims to decompose a polarimetric synthetic aperture (PolSAR) data on a base reflecting some scattering mechanisms. The corresponding coefficients will be further exploited as the feature vector for the subsequent interpretation task. Intuitively, its performance heavily depends on the choice of bases and many off-the-shelf ones have been constructed based on mathematical or physical model since last two decades. However, these fixed bases are generally insufficient to characterize all types of data in a PolSAR image so that the extracted features are not beneficial to the subsequent task. To address this issue, we propose a novel target decomposition framework to learn a set of task-desired bases as well as feature vectors from the input polarimetric data. Focusing on the classification task, involve a supervised regularizer is further involved in our framework to increase the discrimination of features. Experimental results demonstrate the effectiveness of proposed framework.