A deep residual neural network for low altitude remote sensing image classification

In the recent years the applications of deep neural networks are increasing rapidly. There are two important factors determining the efficiency of training a computer vision system using deep neural networks. The first factor is the difficulty of training a very deep neural network with large number of parameters. The second factor is the efficiency of the trained network for decreasing the computational cost. In this paper an efficient deep neural network which uses the grid size reduction, factorization and hyper parameter tuning is proposed. In order to deal with large number of layers the residual units are used. A series of experimental simulations are performed on the application of the proposed deep neural network for classification of aerial images. The experimental results show that the proposed architecture has acceptable accuracy for aerial scene classification.