Unsupervised PolSAR image classification based on sparse representation

ABSTRACT

A novel unsupervised image classification algorithm which based on the sparse representation theory for polarimetric synthetic aperture radar (PolSAR) image is introduced in this paper. The algorithm conjunctively uses sparse representation-based classification (SRC) theory, dictionary updating method, and label smoothness constraint to update class labels. The unsupervised H/$\alpha$/A Wishart classification method is introduced to provide the preliminary classification result, from which the initial dictionary and class labels can be extracted. An energy function is defined, and it contains two terms. The first term is based on the sparse representation theory. It reflects the cost of assigning different class labels to a pixel. The second term is label smoothness constraint. It constrains that class labels of neighbouring pixels in flat regions should be the same. By alternately minimizing the energy function, two unknown variables, dictionary and class labels are updated. Optimized class labels are the outputs to compose the final classification result. Extensive experimental results for three PolSAR datasets are analysed to verify the validity of the proposed method. Comparison with other unsupervised/supervised classification methods indicates its superiority.

Acknowledgments

The authors would like to thank the European Space Agency (ESA) for providing PolSAR datasets, and to the editors and anonymous reviewers for their constructive suggestions. Yaqi Ji acknowledges the support from China Scholarship Council.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by the European Space Agency (ESA) Earth Observation Category 1 under Grant 6613; the 4th Japan Aerospace Exploration Agency (JAXA) ALOS Research Announcement under Grant 1024; the 6th JAXA ALOS Research Announcement under Grant 3170; the Japanese Government National Budget – Ministry of Education and Technology (MEXT) under Grant 2101; Chiba University Strategic Priority Research Promotion Program; Taiwan National Space Organization (NSPO); SOAR-EI Canadian Space Agency (CSA) Project 5436; Indonesian National Institute of Aeronautics and Space (LAPAN).