Polarimetric Inverse Scattering via Incremental Sparse Bayesian Multitask Learning


During this letter, we use the sparse Bayesian multitask learning to understand joint sparsity-imposing polarimetric inverse scattering. The prior assumption regarding the data model is redesigned to avoid information sharing across unrelated tasks. Based mostly on this assumption, we have a tendency to offer the formulas for Bayesian inference still because the algorithm flowchart, which still has the linear complexity. Experimental results demonstrate that polarimetric inverse scattering with the proposed methodology will effectively extract the characteristics of canonical scatterers.

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