PolSAR Image Segmentation by Mean Shift Clustering in the Tensor Space

Abstract We present an unsupervised segmentation algorithm for fully polarimetric synthetic aperture radar (PolSAR) data by using the mean shift clustering. The previous work using the span values of the PolSAR data as the features in the mean shift clustering, however, does not sufficiently exploit the full information contained in the polarimetric covariance matrix. When considering the polarimetric covariance matrices as the feature vectors, the traditional mean shift clustering in the Euclidean space is not applicable anymore, since these matrices do not form a Euclidean space. We first show that by regarding each Hermitian positive definite polarimetric covariance matrix at per pixel as a tensor, the tensor space can be represented as a Riemannian manifold. Then, the mean shift clustering is extended to the Riemannian manifold to explain the theoretical meanings of the tensor clustering and a practical segmentation algorithm based on the metric lying on the manifold is proposed. Experimental results using the real fully PolSAR data and simulated data verify the effectiveness of the proposed method. Key words Polarimetric synthetic aperture radar (PolSAR), image segmentation, mean shift clustering, Riemannian manifold, tensor DOI 10.3724/SP.J.1004.2010.00798 The fully polarimetric synthetic aperture radar (Pol-SAR) involves transmission and reception of both the hori-zontally and vertically polarized radar pulses, thus provide