基于三维卷积神经网络的湖泊提取算法

Lake extraction algorithm based on three-dimensional convolutional neural network

针对现有分析湖泊几何信息算法的二维图像湖泊轮廓提取精度低的问题,提出了一种基于三维卷积神经网络的湖泊提取算法。首先,基于平整度信息从激光扫描点云中定位出候选湖泊并对输入的候选区域点云进行体素化组织,作为神经网络的输入;同时,通过深度学习技术,从候选区域中过滤非湖泊区域;然后,基于方向链码算法从点云中提取湖泊的边缘并分析其几何形状信息。实验结果表明,所提算法在提取激光扫描点云中的湖泊精度可达到96.34%,与当前在二维图像中的湖泊提取算法相比,可对目标湖泊形状信息进行计算与分析,从而为湖泊监测与管理提供方便。

Aiming at the low accuracy of lake contour extraction from two-dimensional images of the existing algorithms for analyzing the geometric information of lakes, a lake extraction algorithm based on three-dimensional convolutional neural network was proposed. Firstly, based on the flatness information, the candidate lakes were located from the laser scanning point clouds, and the candidate points were organized as voxels to be an input of the neural network. Meanwhile, the non-lake areas were filtered from candidate areas by the deep learning technique. Then, based on the chain-code algorithm, contours of lakes were extracted from point clouds and their geometry information was analyzed. The experimental results show that, the accuracy of the proposed algorithm in extracting lakes from laser scanning point clouds is 96.34%, and compared with the existing extraction algorithm for two-dimensional images, the proposed algorithm can calculate and analyze the shape information of lakes, which provides convenience for lake monitoring and management.