Abstract: In order to solve the problem of hole repair in UAV point cloud data, a back-propagation neural networkhole repair method was proposed based on sparrow search algorithm (SSA). The sparrow search algorithm was used to optimize the initial weight and threshold of the traditional BP neural network, and then the BP neural network algorithm (SSA-BP) optimized by the sparrow search algorithm was applied to repair the holes in uav point cloud data. In order to verify the feasibility of the algorithm, the accuracy of SSA-BP neural network was compared with that of traditional BP neural network and least square support vector machine (LSSVM) algorithms. The experimental results show that the repair accuracy of SSA-BP neural network algorithm is higher than the other two groups of comparison algorithms, and the SSA-BP neural network is more stable, and it still has a good repair effect in the repair of complex terrain holes.

Key words: hole repair, sparrow search algorithm, optimization, BP neural network, accuracy comparison