为了进一步提高点云图像船舶分类方法的分类准确率,提出了一种基于三维卷积神经网络(3D CNN)的点云图像船舶分类方法。首先采用密度网格方法将点云图像转为体素网格图像,将体素网格图像作为3D CNN的输入对象;接着通过设计的6层3D CNN提取体素网格图像的高水平特征,捕捉结构信息;最后在输出层利用Softmax函数进行分类,得到最终的分类结果。实验结果表明,在自建的点云图像船舶数据集上,所提方法的分类准确率达到了96.14%,比3D ShapeNets方法和VoxNet方法分别提高了5.97%和2.46%。在悉尼城市目标数据集上,与现有一些方法相比,所提方法的分类准确率较高。这些结果均证明所提方法具有良好的分类性能。

In order to further improve the classification accuracy of ship classification method for point cloud images, a new ship classification method based on three-dimensional convolutional neural network (3D CNN) is proposed. First, the point cloud image is transformed into a voxel grid image by the density grid method and the voxel grid image is taken as the input object of a 3D CNN. Then, the high-level features of the voxel grid image are extracted by the designed 6-layer 3D CNN to capture its structural information. Finally, the classification results are obtained using the Softmax function in the output layer. The experimental results show that the classification accuracy of the proposed method can reach 96.14% on the self-build point cloud image ship dataset, 5.97% higher than that of the 3D ShapeNets method and 2.46% higher than that of the VoxNet method. Compared with some existing methods, the proposed method has higher classification accuracy on Sydney urban object dataset. These results show that the proposed method has a good classification performance.