# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 2 The Proposed Methodology
### Data Processing  
This paper conducts maritime exploration using X-band radar deployed in the Qiongzhou Strait. The location of the radar deployment and related information are shown in Figure 3 and Table 1. After receiving the echo data, it is transmitted from the development board to the computer via a fiber optic connection, and we first perform matched filtering. Subsequently, the CA-CFAR algorithm is employed for target detection; CA-CFAR estimates the background noise level by calculating the average power of the reference cells surrounding the unit under test, and it can dynamically adjust the detection threshold to maintain a constant false alarm rate. Following this, the radar point cloud is clustered using the algorithm outlined in reference [2], which allows us to identify several target regions. The aim of this study is to classify these target regions based on their attributes; thus, cargo ships, vessels, yachts, and other similar objects on the sea are all considered maritime targets without distinguishing between specific types of vessels, only categorizing them as either maritime or terrestrial targets. We delineate the maritime and terrestrial areas based on the coastline displayed on electronic navigational charts and annotate the samples located in different regions as maritime targets or terrestrial targets. The detailed information for the final dataset is presented in Table 2. The weather conditions during the observation period are also recorded in the table. The observation period spans four days (designated as training sets D1-D2 and test sets D1-D2), with each observation focusing on a fixed area. We also retain the original radar echo data to augment the sample data in the dataset. As mentioned earlier, the objective of this paper is to conduct attribute analysis on clustered point cloud data to determine whether they belong to the sea-surface target or land target category. Therefore, the input of the neural network is the raw return intensity of the point cloud in the specific area. To facilitate further analysis and comparison, we also performed region filling on the returns within specific observation windows. The entire data processing process is as follows:  
1. Perform matched filtering and CA-CFAR detection on the radar echo data to obtain a frame of radar point cloud data, including the detected target point positions and and their echo intensities. 2. Use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm described in [11] to cluster and merge the target points into clusters. The specific steps are as follows: 1. Initialize the distance threshold \(D_{ls}\) and the target number threshold \(P_{ts}\). 2. Randomly select a detected target point, and use this point as the center to search for neighboring target points within a radius of \(D_{ls}\). 3. If the number of neighboring target points \(N_{ts}>=P_{ts}\), then the selected target point is considered a core point. 4. Then, randomly select another target point from the remaining target points and determine whether it is a core point. 5. Repeat the previous step until all the target points in the current frame have been traversed. 6. Traverse each core point, search for neighboring core points within the distance threshold Dis, and classify them into the same cluster. 3. For each cluster, define the bounding rectangle based on the boundary of its core points. Each cell in the matrix corresponds to a target point within the cluster, with the cell value representing the echo intensity of the corresponding target point in the matched filtering data. The cell values of non-target point positions in the rectangle are set to 0. 4. Obtain the center coordinates of each bounding rectangle. In the rectangle, reverse search the corresponding echo intensity in the matched filtering data. Fill the coordinate cells within the bounding rectangle that have a value of 0 with the echo intensity retrieved from the corresponding positions.