Knowledge and data co-driven deep learning model for PolSAR image classification

• Aknowledge and data co-driven model is proposed for PolSAR image classification. • Physics-guided module is proposed to extract features with physical significance. • Physics-constrained module ensures physical consistency of classification results. • Experimental results show proposed method outperforms other methods. Deep learning method has become the mainstream technology for polarimetric synthetic aperture radar (PolSAR) image interpretation in recent years. However, traditional deep learning-based approaches to PolSAR image classification are mostly data-driven, and the prediction results are highly dependent on the quantity of labeled samples, while ignoring the potential physical characteristics of PolSAR data. Moreover, owing to the unique microwave imaging mechanism in PolSAR technology, some targets with complex scattering are difficult to distinguish visually. Therefore, a deep learning model driven by knowledge and data is proposed for PolSAR image terrain classification. This model embeds physical knowledge into deep neural network and is guided to learn high-level semantic features with physical perception capabilities. Meanwhile, in the output space, according to the general physical scattering laws followed by PolSAR terrain targets, the corresponding regularized loss function is designed to constrain the network to learn. This design helps to train models with limited training samples, promotes the rapid convergence of the deep learning model, and improves the accuracy of PolSAR image classification under limited labeled samples. Comprehensive experiments are performed on three widely utilized PolSAR datasets to validate the efficacy and superior performance of the proposed approach.