# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 3 Coarse-Level Focusing and Classification of MEO SAR Ship Targets
### Ship Target Coarse Classification Based on Global Features  
In the first-level coarse classification, ship targets are categorized into three major classes: civilian vessels, other military ships, and aircraft carriers. The classification is primarily based on the target's global morphological characteristics. Aircraft carriers, as typical large military vessels, exhibit significant morphological and dimensional distinctions from other ships. These distinctive features become markedly apparent after coarse focusing. Consequently, aircraft carriers are classified as a separate category during the coarse-classification stage. Other military ships consist of destroyers and cruisers. These two classes exhibit high global similarity, with comparable characteristics in terms of dimensions, length-to-width ratios, and bow and stern structures. After coarse focusing, they can be classified as military ships based on contour morphological features. Civilian ships, including bulk carriers, container ships, and tankers, also demonstrate similar typical civilian ship characteristics, such as rounded bows and concentrated length-to-width ratio distributions smaller than military ships. In summary, there is a large gap between these three categories after coarse focusing, providing adequate features for discrimination. At the same time, intra-class variations within each coarse class remain sufficiently small, which will not affect the global feature representation of the target. Classification is achieved based on global feature extraction, which enables rapid pre-classification of ship targets into these broad categories. The ResNet algorithm [31; 32; 33] is a deep neural network architecture renowned for its significant advantage of enabling cross-layer residual learning. This capability substantially reduces the number of parameters, thereby decreasing the computational load of deep networks. In this paper, the ResNet-101 network is employed as the main component of the coarse-classification model. The specific parameter structure of each layer, the dimensions of the output vectors, and the total number of parameters are detailed in Table 3.  
The globally salient morphological features of the ship targets are utilized to refine the coarse target classification results by the ResNet network. The aspect ratio, serving as a critical feature for ship targets, is widely utilized in target identification tasks. By integrating the grayscale characteristics of targets in SAR imaging results, the aspect ratio and the mean pixel value are employed as two key features to refine the coarse classification results. Firstly, [PERSON]'s thresholding is applied to the target slice to remove background interference and extract the target region data. This step effectively separates the target from the background in the SAR image. Subsequently, statistical analysis is performed on the pixel values of the target to obtain the mean pixel value. Ship targets often exhibit certain angles. When ship targets are not aligned horizontally or vertically in the SAR image, traditional horizontal bounding boxes fail to closely fit the targets, resulting in inaccurate aspect ratios. Therefore, a minimum bounding rectangle is employed to select the targets, ensuring a better fit to the target contours and improving the accuracy of the calculated aspect ratios. The above two features are then quantified by statistically analyzing a portion of the training data during the training process. The analysis results are used to correct biases in the training results, thereby guiding the network's training outcomes to converge toward the target standard features. This enables feature-guided coarse classification of ship targets. The number of sample categories in the training set is three. Let \(Z\) be the number of samples per category. For the \(i\)-th image data sample of the \(j\)-th category, the pixel mean value is \(PMV_{j,i}\), and the aspect ratio is \(AR_{j,i}\).