SAR images classification method based on Dempster-Shafer theory and kernel estimate

To study the scene classification in the Synthetic Aperture Radar (SAR) image, a novel method based on kernel estimate, with the Markov context and Dempster-Shafer evidence theory is proposed. Initially, a nonparametric Probability Density Function (PDF) estimate method is introduced, to describe the scene of SAR images. And then under the Markov context, both the determinate PDF and the kernel estimate method are adopted respectively, to form a primary classification. Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification. Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification. Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results. Experimental results on real SAR images illustrate a rather impressive performance.

SAR images classification method based on Dempster-Shafer theory and kernel estimate

To study the scene classification in the Synthetic Aperture Radar (SAR) image, a novel method based on kernel estimate, with the Markov context and Dempster-Shafer evidence theory is proposed. Initially, a nonparametric Probability Density Function (PDF) estimate method is introduced, to describe the scene of SAR images. And then under the Markov context, both the determinate PDF and the kernel estimate method are adopted respectively, to form a primary classification. Next, the primary classification results are fused using the evidence theory in an unsupervised way to get the scene classification. Finally, a regularization step is used, in which an iterated maximum selecting approach is introduced to control the fragments and modify the errors of the classification. Use of the kernel estimate and evidence theory can describe the complicated scenes with little prior knowledge and eliminate the ambiguities of the primary classification results. Experimental results on real SAR images illustrate a rather impressive performance.