# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 1 Introduction  
Radar is one of the primary means of monitoring maritime targets [1, 2, 3]. Owing to its unique operating principles, radar can effectively detect and track targets under various weather conditions. This capability stands in stark contrast to electro-optical systems, which are often limited by lighting and meteorological factors; for instance, adverse weather conditions such as fog, heavy rain, or strong winds significantly impair their performance [4]. Therefore, radar plays an indispensable role in fields such as maritime surveillance, military reconnaissance, aerospace applications, and shipping safety due to its all-weather operational capability [5]. Furthermore, with continuous technological advancements, radar technology is evolving, expanding its application potential in target identification, data fusion, and intelligent analysis. However, existing target detection algorithms [6, 7, 8, 9] for X-band pulse primarily achieve binary detection, meaning they can only determine the presence or absence of a target without accurately classifying its type, as shown in Figure 1. This limitation poses numerous challenges in practical applications, particularly in complex maritime environments, where relying solely on binary detection may lead to false alarms or missed detections, thereby affecting the scientific and effective nature of decision-making. For example, during maritime surveillance operations, failing to differentiate between types of vessels couldadversely impact law enforcement actions or resource allocation, wasting precious monitoring resources and potentially creating safety hazards. Consequently, enhancing the target classification capability of radar systems has become an urgent issue that needs addressing to meet increasingly complex maritime monitoring demands. Researchers must explore more advanced and effective algorithms to enable these systems to simultaneously perform the dual tasks of target detection and classification. For land-based maritime surveillance radars, the monitored area not only includes vessels on the water's surface but often also involves various terrestrial and island targets. These different types of targets possess unique attributes and characteristics, making it crucial to conduct in-depth attribute analysis of detected targets. By accurately determining the category in which a target belongs, an efficient monitoring system can better support decision-making processes in practical law enforcement and emergency response. For instance, swiftly identifying the type of vessel (such as fishing boats, commercial ships, or military vessels) during marine patrols directly influences the degree of and approach to law enforcement, ensuring maritime safety and order. Additionally, monitoring surrounding land and island targets enhances overall security measures, enabling timely identification and response to potential risks. Moreover, clarifying the properties of targets can support multidimensional data analyses, providing a scientific basis for strategic planning and resource allocation. Our approach involves utilizing X-band pulse radar echoes for preliminary exploration of sea-surface target category recognition (such as cargo ships or fishing vessels), first distinguishing between sea-surface targets and land targets, which is very beneficial for the fine classification of sea-surface targets. This paper enhances the informational breadth of existing binary detection methods by proposing a radar target attribute recognition method based on dynamic dense connections. Researchers have proposed clustering [11; 12; 13; 14] and sea-land segmentation methods [15; 16; 17; 18; 19] based on radar echoes. Reference [11] proposes pre-sampling the original data to reduce data volume, thereby minimizing computation time during the clustering process. References [12; 13] employ an automatic parameter adjustment method to enable the clustering algorithm to adapt to the distribution characteristics of large-scale datasets.