Abstract
To analyze the visibility of remote monitoring objects based on the data of different-time images in the visible and infrared wave ranges, it is important to know the patterns of temperature changes both of the Earth's surface itself and of objects. The study of the dynamics of temperature changes using a mathematical model of heat transfer allows us to obtain additional unmasking feature. As an additional unmasking feature, numerical estimates of the thermophysical parameters of hidden subsurface objects can be calculated based on the application of a genetic optimization algorithm. Grouping thermophysical parameters into separate classes allows you to highlight the boundaries of objects and unmask the area of their occurrence under the ground. To solve the problem of correct processing of a large amount of data, reducing the processing time of aerial photographs, it is advisable to use the method of machine learning based on neural networks. The article presents the process of segmentation of objects using deep learning, a mathematical model of thermophysical processes is formed to obtain numerical estimates of the thermophysical parameters of hidden subsurface objects in the course of solving the coefficient inverse problem of thermal conductivity based on a genetic algorithm.
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