A Multifeature TDAO-AIR Clutter Classification Approach for Inshore Ambiguity Identification and Suppression With Azimuth Multichannel SAR System

In the inshore region, the azimuth ambiguity of land clutter is the main factor, which reduces the clutter suppression performance for the multichannel synthetic aperture radar (SAR) system. To address this problem, we propose a multifeature tensor discriminant alternating optimization and affine invariant Riemannian (TDAO-AIR) classification approach. First, the correlation coefficient between channels is obtained to characterize the normalized SAR image amplitude. Next, multilook interferometric processing is performed to reduce the effect of Speckle noise and acquire multilook interferometric complex images. At the same time, we extract the correlation coefficient gradient and the multilook interferometric phase gradient in different spatial directions to characterize the spatial structure. Next, we construct a feature tensor (FT), including correlation coefficient between channels, multilook interferometric phase, and spatial structural gradient information for each range-Doppler unit (RDU). The TDAO algorithm is proposed to extract the core FT (CFT) by optimizing the objective function and getting the projection matrix. Then, the CFT is unfolded along the feature dimension, and the feature covariance matrix (FCM) is constructed for each RDU. With the AIR distance measure, the inshore region is divided into different clutter regions. Finally, in the azimuth ambiguous clutter region, independent and identically distributed (IID) training samples are selected to estimate the clutter covariance matrix (CCM), and robust adaptive clutter suppression is performed in the SAR image domain. The experiments on simulated and real measured data by TerraSAR-X demonstrate that the proposed method can accurately identify and suppress the azimuth ambiguity in the inshore region.