Robust Semisupervised Classification for PolSAR Image With Noisy Labels.

The robustness of the supervised polarimetric synthetic aperture radar (PolSAR) image classification is severely affected by two main aspects, namely, the quantity and quality of the labeled training pixels. Specifically, limited manually labeled pixels with respect to the large scale of PolSAR image have limited the performance of the automatic classification methods, while manually labeled training pixels shall be unfaithful with the speckle and impure cell for their low qualities. In order to address the above two fundamental problems, we propose a robust semisupervised probability graphic-based classification framework. First, a semisupervised learning scheme is implemented to simultaneously exploit both labeled and unlabeled pixels for information compensation. Moreover, structural relationship among neighboring pixels inducing from the prior information is further benefit to reduce the influence of limited labeled pixels. Second, a robust classification loss function is added in the process of training classifier to enhance the robustness to the noisy labeled pixels. Third, unfaithful limited labeled data can be settled with a hybrid generative/discriminative classification framework, where labeled and unlabeled pixels are simultaneously exploited for learning high-level feature for the low-quality pixels. The effectiveness of the proposed framework on the specific aspect is validated in experiments on real PolSAR data sets, which reveal the superiority in both visual performance and classification accuracy compared with the state-of-the-art methods. Totally speaking, our model has improved the classification accuracy by at least 20% on data set Flevoland, 10% on Oberpfaffenhofen, and 5% on Weihe River than the compared ones. [ABSTRACT FROM PUBLISHER]