Classification method for spatial targets based on convolutional neural network

In this paper, we constructed a one-dimensional convolutional neural network as a classifier model for spatial object classification. Considering that there are few available training samples obtained from actual measurement, combining with the characteristics of actual measurement data, we simulated a large amount of data for training and testing. The simulation results show that our method has a high classification accuracy and can overcome the problems existing in actual measurement, such as tracking mixed batches to a certain extent, and it can also effectively solve the problem that it is difficult to directly train neural networks because of the small number of spatial target samples, which take advantage of neural network autonomous learning and memory to reliably identify features.