TNN-STME: A Matrix Decomposition Method for SAR Ship Real-Time Detection Using 2-D Asymmetric Resolution Mode - Chaoyue Liu; Yunkai Deng; Zhimin Zhang; Huaitao Fan; Heng Zhang; Xiangyang Qi; Wei Wang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Maritime ship detection is an essential prerequisite for targeting key objectives and safeguarding the security of territorial waters. The all-day and all-weather operational capability of synthetic aperture radar (SAR) makes SAR-based ship detection indispensable for maritime surveillance. Marine surveillance requires extensive coverage for large-scale searches. But there is a contradiction between high resolution and wide swath, which cannot be taken into account. Most existing marine surveillance modes generally exchange extensive coverage at the expense of spatial resolution. The 2-D detailed information is lost in this way, which is not conducive to target classification. Furthermore, real-time detection becomes a challenge under the constraints of limited on-board conditions. In this article, a novel marine surveillance mode is proposed. It divides the target search task into two parts: wide-area target detection in 2-D asymmetric resolution mode (2-D-ARM) and key target focusing in 2-D high-resolution mode. In the context of 2-D-ARM, the sparsity of the targets and the joint sparse low-rank characteristics of the background are studied, and a low-rank approximation model is developed to accomplish the task of real-time ship detection. The experiments show that the proposed method can realize the sparse target matrix extraction (STME) through matrix decomposition. Using Sentinel-1A, Japanese Advanced Land Observing Satellite (ALOS) PALSAR, and Gaofen3 single-polarization single-look complex data, the 2-D-ARM imaging is simulated, and a dataset is built to verify the processing performance of the proposed method.