PolSAR Ship Detection Based on Kernelized Support Tensor Machine

Using polarimetric synthetic aperture radar (PolSAR) imagery for ship detection is a critical research area in marine surveillance. Currently, the mainstream methods primarily fall into two categories: superpixel approaches and neighborhood matrix methods. These methods aim to utilize both the polarimetric and spatial information of the neighborhood pixel patch for detection. However, existing methods may not fully exploit the potential of neighborhood information. This letter formulates the ship detection problem as a binary classification task and introduces an innovative ship detection algorithm based on kernelized support tensor machine (K-STM). By employing neighborhood polarimetric tensors as the feature representation of the pixel patch, we can implicitly incorporate all polarimetric and spatial information within different dimensions of the tensor. With the help of the tensor kernel function, K-STM can effectively extract feature information embedded in the neighborhood polarimetric tensors across different dimensions. Two PolSAR datasets acquired from Radarsat-2 are used for experimental validation. The proposed K-STM method achieves the highest figure of merit (FoM) of 0.898 and 0.975 for two datasets. It demonstrates that the proposed method can achieve better performance on ship detection.