PolSAR Image Classification Based on Object-Based Markov Random Field With Polarimetric Auxiliary Label Field

Recently, an object-based Markov random field (OMRF) with auxiliary fields (OMRF-A) was developed for the processing of optical remote sensing images and it provided satisfactory results. However, it cannot be directly applied to polarimetric synthetic aperture radar (PolSAR) images with high heterogeneity. In addition, polarimetric information which plays a dominant role in PolSAR image interpretation cannot be effectively incorporated by the OMRF-A model. In order to solve this problem, an OMRF with polarimetric auxiliary fields (OMRF-PA) is developed in this letter for the classification of PolSAR images. A polarimetric index is developed to evaluate the information loss during iterations of label fields and auxiliary label fields. Then, an improved conditional probability distribution which incorporates the polarimetric index is proposed to account for the interactions between label fields and auxiliary label fields. Experiments on PolSAR data sets acquired by ESAR and EMISAR systems demonstrate that the proposed OMRF-PA model can generate a higher classification accuracy compared to the original OMRF-A method.