# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 1 Introduction  
The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has advantages such as short visit time and wide coverage, which can significantly improve real-time observation capabilities [1, 2]. These features are particularly valuable for wide-swath maritime ship target surveillance in both civilian and military scenarios. Traditional ship target detection and recognition procedures typically consist of two main steps. Firstly, SAR image formation is conducted to acquire the full-scene focused SAR image. Then, target detection [3, 4, 5] and recognition [6, 7, 8, 9] methods are applied to the focused image to acquire further ship target information. Compared to Low Earth Orbit (LEO) SAR system, MEO SAR has higher orbital altitude and longer synthetic aperture time. This consequently introduces complex range cell migration (RCM) and spatially variant echo parameters, making full-scene imaging processing highly complex [10, 11]. In addition,motion errors induced by ship movements also make target ships severely defocused in MEO SAR images [12; 13]. These issues cause increased computational complexity and reduced precision for traditional ship target detection and recognition methods when processing MEO SAR data. Performing ship target detection in the range-compressed domain is an effective way to improve detection efficiency, especially for open-sea scenarios [14; 15]. Extracting target ship signals in the range-compressed domain can significantly reduce the data that need to be processed. In order to achieve further ship classification, it is still necessary to focus the range-compressed data to obtain the ship image [16]. However, moving ship targets may suffer from defocusing in SAR images, as traditional imaging algorithms are designed for stationary scenarios. Many refocusing methods for moving ship targets in SAR images have been proposed. The conventional phase-gradient autofocus (PGA) method employs a strong scatter point approach to estimate phase errors induced by target movements [17; 18; 19]. Since motion errors cause SAR image defocusing, some algorithms estimate phase errors by optimizing SAR image quality, such as by maximizing image contrast or minimizing image entropy [20; 21]. For MEO SAR systems, with increasing synthetic aperture time, the degree of defocusing caused by ship rotations becomes increasingly severe [22]. The above-mentioned refocusing methods cannot estimate and correct space-variant motion errors effectively [23; 24]. This problem can be solved by dividing SAR image into multiple sub-blocks and refocusing them separately [25; 26; 27; 28]. But this strategy faces the challenge of increasing computational load. Therefore, achieving both efficient and effective ship target fine recognition still remains a critical challenge for MEO SAR systems. To address the above difficulties, a novel multi-level focusing-classification strategy for MEO SAR systems is proposed in this paper to realize ship target fine recognition. The proposed method is applied to detected range-compressed ship data. The main idea is to decompose the complex ship focusing process into fast global coarse-focusing and local fine-focusing and to design corresponding coarse classification and fine classification networks for these two focusing results. Based on the coarse classification results, the target-specific local region image slices are extracted for fine focusing and classification, which greatly reduces the computational load required for global fine-focusing. A diagram of the proposed MEO SAR ship target recognition strategy is shown in Figure 1. The primary task involves classification of six types of ships, including aircraft carriers, cruisers, destroyers, tankers, container ships, and bulk carriers. The ship target multi-level focusing-classification strategy contains the following steps. 1. **Coarse focusing**: Ship targets are coarsely focused on by estimated Doppler parameters. Motion errors induced by target sailing velocity are compensated for in this step. 2.