Choosing Hyperparameter Values of the Convolution Neural Network When Solving the Problem of Semantic Segmentation of Images Obtained by Remote Sensing of the Earth's Surface

摘要

Among the tasks solved by artificial neural networks are the tasks of analyzing objects on the images of the underlying Earth's surface, obtained by the on-board equipment of unmanned aerial vehicle (UAV). For the solution of such problems, the convolutional neural networks (CNN), operating semantic segmentation of the received image, are widely used. In this case, the designer of such networks has to solve the difficult task of selecting hyperparameter values for them. These values' choice is one of the most critical tasks that have to be solved when forming a CNN. Existing attempts to solve this problem are usually based on one of two approaches. The first one involves a set of experiments with different values of hyperparameters of the CNN with learning each of the network variants. These experiments are performed until a CNN with acceptable characteristics is obtained. This approach is simple to implement but does not guarantee a CNN with high performance. The second approach treats the selection of hyperparameter values in the network as an optimization problem. If this problem is successfully solved, it is possible to obtain a CNN with sufficiently high characteristics. However, this task has a significant complexity and also requires a large consumption of computing resources. Images in the form of multidimensional arrays are used as source data to analyze objects on the underlying surface. It means that CNN will contain a significant number of parameters. Accordingly, it will take considerable time to find a suitable CNN by searching for possible hyperparameter values. This paper proposes an alternative approach to the problem of selecting the hyperparameter values of CNN based on the analysis of the processes running in the network. The effectiveness of this approach is demonstrated by solving the problem of semantic segmentation of the underlying surface obtained by remote sensing of the Earth's surface.