Ship Detection in Polarimetric SAR Images via Variational Bayesian Inference

Abstract

In this paper, we propose a novel ship detection approach in polarimetric synthetic aperture radar (SAR) images via variational Bayesian inference. First, we express the polarimetric SAR image as a tensor, and decompose the SAR image as the sum of a sparse component associated with ships and a sea clutter component. These components are denoted by some latent variables. Then, we introduce hierarchical priors of the latent variables to establish the probabilistic model of ship detection. By using variational Bayesian inference, we estimate the posterior distributions of the latent variables. Finally, the ship detection result is obtained in the iterative Bayesian inference process. By virtue of the tensor representation of polarimetric SAR image, the proposed approach explicitly uses all the polarization channels of the SAR image, and avoids the possible information loss in scalar polarimetric feature representation. Moreover, the proposed approach needs no sliding windows. The variational Bayesian inference process actually uses all the pixels instead of the limited pixels in sliding windows. Thus, the proposed approach has good ship detection performance and shape preserving ability, which is especially suitable for congested sea areas. Experimental results accomplished over C-band RADARSAT-2 polarimetric SAR images demonstrate that the proposed approach can achieve state-of-the-art ship detection performance.