# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 4 Conclusions  
Radar plays a crucial role in monitoring maritime targets, and it is essential to distinguish between sea-surface and land targets before performing fine classification. This differentiation greatly aids in improving subsequent fine classification. To address this, this paper proposes a radar target classification method using dynamic dense connections. The proposed method takes into full consideration the characteristics of pulse compression radar echoes and the task of target attribute recognition, enabling it to enhance existing binary detection methods. The experimental results show that this approach surpasses current deep network architectures in attribute recognition performance. Of course, the method  
Figure 5: Comparison of attribute recognition results using different different neural networks. (**a**) Ground truth; (**b**) result for LeNet; (**c**) result for ResNet; (**d**) result for GoogleNet; (**e**) result for DenseNet; (**f**) result for DynseNet.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Accuracy**} \\ \cline{3-6}
**Index** & **Model Variant** & \multicolumn{2}{c}{**Unfilled**} & \multicolumn{2}{c}{**Filled**} \\ \cline{3-6}  & & **D1** & **D2** & **D1** & **D2** \\ \hline \#1 & LeNet & 0.6321 & 0.6384 & 0.7420 & 0.8738 \\ \#2 & ResNet & 0.8672 & 0.7984 & 0.8968 & 0.9545 \\ \#3 & GoogleNet & 0.8934 & 0.8068 & 0.9332 & 0.9555 \\ \#4 & DenseNet & 0.8949 & 0.8268 & 0.9476 & 0.9636 \\ \#5 & DynseNet & **0.9067** & **0.8316** & **0.9636** & **0.9757** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results.  
proposed in this paper also has certain limitations, including high computational complexity and a current inability to achieve multi-target recognition. Future work will focus on further improving the proposed method's ability to capture subtle differences between different target types by exploring advanced techniques such as reinforcement learning, enhancing multi-target recognition, and thereby refining the method. Additionally, incorporating multi-sensor data fusion strategies to combine radar information with other sensor modalities could enhance target classification accuracy in complex maritime environments. Moreover, investigating real-time implementation on embedded systems for practical deployment will be a key area for future research.  
Conceptualization, J.W. and P.L.; methodology, J.W.; validation, K.C. and T.X.; data processing and analyzing, J.W. and T.X.; resources, P.L.; data curation, T.X.; writing--original draft, K.C. and T.X.; writing--review and editing, J.W. and P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by a Hainan Province Science and Technology Special Fund under Grant ZDYF2025 SHFZ058, in part by the Youth Innovation Promotion Association, Chinese Academy of Sciences, under Grant 2022022, in part by the South China Sea Nova project of Hainan Province under Grant NHXMRCXM202340, and in part by the Haikou Key Science and Technology Project under Grant 2024020.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We appreciate [PERSON]'s assistance during the paper revision phase.  
The authors declare no conflicts of interest.