Cross-Domain Density Map-Generated Ship Counting Network for Remote Sensing Image

Yaxiong Chen;Qijian Li;Kai Yan;Shengwu Xiong
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Abstract

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.
面向遥感图像的跨域密度图船舶计数网络
近年来,随着遥感技术的不断发展,海上船舶监测已成为一个重要的研究领域。准确统计遥感影像中的船舶数量对海上交通安全、渔业管理和海洋环境保护至关重要。现有方法通常使用高斯核函数生成密度图;然而,由于船舶形状各异,不符合高斯核,因此得到的密度图不能准确反映船舶的真实形态,从而影响计数性能。为了克服这些限制,我们引入了跨域密度地图生成船舶计数网络(CDDMNet)。该网络创新性地融合了跨域特征融合模块(CDFFM),能够有效地适应不同尺寸和形状的船舶。此外,我们引入了特征相关正则化约束(FCRC)和积分损失函数,有效克服了船舶尺寸变化可能带来的干扰,增强了模型对船型和环境条件变化的适应性。实验结果表明,CDDMNet在多个遥感图像数据集上取得了优异的性能。最后,在RSOC数据集上,平均绝对误差(MAE)达到52.80,均方根误差(RMSE)达到69.77。
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