Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images

Abstract

Several features have been developed to characterize the land cover in synthetic aperture radar (SAR) data with speckle noise. Feature extraction has become an essential task for SAR image processing. However, how to preserve the original intrinsic structural information and enhance the discriminant ability to reduce the impact of noise is still a challenge in this area. In this paper, using a clustering method to maintain the nonlocal information in images and tensors with the ability to preserve spatial neighborhood structure information, a new cluster-based tensorial semisupervised discriminant analysis (CTSDA) method is proposed for feature extraction of SAR images. In the CTSDA, the block clustering algorithm is employed to generate several high-order clustering tensors of multifeature SAR images, which preserves the intrinsic nonlocal spatial information and neighborhood structure. In the multiple manifold structures of the cluster tensors, the improved discriminant analysis enhances the feature discrimination by considering the local structure and labeled and unlabeled information through the Laplace matrix, and the fusion of tensor algebraic analysis and improved discriminant analysis produces multiple new projection directions of the cluster tensors. Finally, feature extraction is achieved by rearranging the projected cluster tensors. The experimental results on the simulated SAR data and four real SAR images demonstrate the superiority of the proposed method over several state-of-the-art approaches.

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

“…Although, increased polarimetry lowers the effective spatial purity of the backscatters, the dual-channel polarimetry provided by S1-SAR sufficiently balances the spatial and polarimetric requirements [10], [24]. Also, the dual polarisation allows for accurate discrimination of specular and diffuse surface scatters and as a result, they are essential in describing the urban patches using simple measures such as mean, median, and standard deviation [32].…”