Satellite platform classification method based on deep neural network using photometric data

There is a high correlation between an object's photometric data and its shape, size, material, and movement. In order to classify the type of satellite platform using the photometric data, a deep neural network based satellite platform classification method is proposed. Preprocessing techniques like as interpolation, smooth filtering, distance correction, and phase correction are applied to the photometric data needed for network training. Convolutional long short-term neural network is constructed to extract the spatial and temporal features of space objects from photometric data, and the average classification accuracy of rocket, satellite Iridium, satellite GlobalStar and space debris is 73.8%, better than the 70.43% accuracy of the convolution neural network. Additionally, the deep neural network is further tested using simulated photometric data from seven satellite platforms, including White Cloud (WC), defense meteorological satellite program (DMSP), future imagery architecture (FIA), and geosynchronous space situational awareness program (GSSAP). The classification accuracy of the satellite platforms is significantly increased to better than 90%.