Unsupervised polsar image factorization with deep convolutional networks

This paper presents a novel unsupervised polarimetric synthetic aperture radar (PolSAR) image classification method, which incorporates polarimetric image factorization and deep convolutional networks into a principled framework. To implement this idea, we design a convolutional neural network (CNN) with a newly defined loss function which measures the probability distribution distance between the initial distribution maps and CNN predictions. In the proposed method, we firstly execute polarimetric image factorization to generate a dictionary of meaningful atom scatters and their corresponding distribution maps, where the strongest scatters are selected as training samples for CNN. Next, we train the CNN by iteratively optimizing the defined energy function, producing the final distribution maps and classification result. The proposed approach is applied on a real UAVSAR image. Experimental results justify that our approach can effectively classify the PolSAR image in an unsupervised way and produce favorable classification results.