A New GMRF Self-supervised Algorithm Applied to SAR Image Classification

The problem of limited tagged training samples and unknown the number of classes is challenging for the classification of remote sensing scenes. This paper presents a new GMRF self-supervised algorithm for SAR image. We add a GOF process in the process of estimating GMM parameters by EM algorithm, which can not only dynamically select the best number of significant classes but also provides an initial feature parameter to calculate the MRF minimum energy. After the iterative region label and region growth cycle, iteration is combined with the Mll context model to obtain the best mark of each region. Since the initial feature parameter selection of the MRF is not random, the operation efficiency is also improved while reducing the number of iteration cycles of the algorithm. The experiment validates that our design not only solves the problem of manual input of the number of classes but also provide the better output result graph in terms of detail maintenance than the expert interpretation of the truth map in the unsupervised image classification process, and we hope that it could support operation and meet the real-time requirements.