基于张量空间中的均值漂移聚类的极化SAR图像分割(英文)

• 作 者: 王英华 韩崇昭
• 机 构: Institute of Integrated Automation,Xi'an Jiaotong University,
• 出 处: 自动化学报
• 发表日期: 2010年06期
• 关键词: 张量空间 均值漂移 covariance clustering segmentation Euclidean Riemannian aperture manif

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 effiectiveness of the proposed method.

We present an unsupervised segmentation algorithm for fully polarimetric synthetic aperture radar (PolSAR) data by using the mean shift clustering. However, does not Λ 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. Chen, 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 u sing the real fully PolSAR data and simulated data verify the effiectiveness of the proposed method.