首页|Cross-Domain Density Map-Generated Ship Counting Network for Remote Sensing Image
Cross-Domain Density Map-Generated Ship Counting Network for Remote Sensing Image
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NETL
NSTL
IEEE
In recent years, with the continuous development of remote sensing technology, maritime ship monitoring has become an important research area. Accurately counting the number of ships in remote sensing images is crucial for maritime traffic safety, fisheries management, and marine environmental protection. Existing methods typically use Gaussian kernel functions to generate density maps; however, due to the varied shapes of ships that do not conform to the Gaussian kernel, the resulting density maps fail to accurately reflect the true forms of ships, thereby affecting counting performance. To overcome these limitations, we introduce the cross-domain density map-generated ship counting network (CDDMNet). This network innovatively incorporates a cross-domain feature fusion module (CDFFM), which effectively adapts to ships of varying sizes and shapes. In addition, we have introduced the feature correlation regularization constraint (FCRC) and the integrated loss function, which effectively overcome the disturbances that may arise from variations in ship sizes and enhance the model’s adaptability to changes in ship types and environmental conditions. Experimental results show that the CDDMNet has achieved excellent performance across multiple remote sensing image datasets. Finally, on the RSOC dataset, the mean absolute error (MAE) reached 52.80 and the root mean squared error (RMSE) reached 69.77.
High Field Laboratory for Superconducting Materials, Institute for Materials Research, Tohoku University, Sendai, Japan
High Field Laboratory for Superconducting Materials, Institute for Materials Research, Tohoku University, Sendai, Japan|Department of Materials Science (Faculty of Engineering), Kyushu Institute of Technology, Kitakyushu, Japan
Toshiba Energy System and Solutions Corporation, Yokohama, Japan
NETL
NSTL
IEEE
