Usage of Intelligent Methods for Multispectral Data Processing in the Field of Environmental Monitoring

Abstract

The subject of study in the article is artificial intelligence methods that can be used for recognition of specific areas of the earth's surface in multispectral images provided by Earth remote sensing systems (ERS). The goal is to automate data analysis for recognizing areas affected by fire on multispectral remote sensing images. The task is to study and formulate a method for processing multispectral data, which makes it possible to automate the process of operational recognition of areas of burned-out areas in images, for the development of an eco-monitoring software system using artificial intelligence tools such as deep learning and neural networks. As a result of the analysis of modern methods of processing multispectral data, an investigation of the supervised learning strategy was chosen. The choice of the described method for solving an applied problem is based on the high flexibility of these method, as well as, provided that there is a sufficient amount of used training input data and correct training strategies, the possibility of analyzing heterogeneous multispectral data with ensuring high accuracy of results for each individual sample. Conclusions: the application of methodologies for intelligent processing of multispectral images has been investigated and substantiated. The theoretical foundations of the construction of neural networks are considered, the applied area of ​​application is selected. An architectural model of a software product is analyzed and proposed, taking into account its scalability, the model of software system is developed and the results of its work are shown. The obtained results show the efficiency of proposed system and prospects of the proposed algorithms, which is a reason for further research and improvement of the used algorithms, with their possible use in industrial and enterprise eco-monitoring systems.

Devising a method for balancing the load on a territorially distributed foggy environment

“…The process of modeling the distribution of additional load is based on the graph representation of tasks entering virtual clusters. As a result, a generalized functional (19) was formed for the objective function (20) of the load transfer problem to an additional virtual cluster. Optimization is carried out according to the criterion of reliability with strict time constraints specified by formula (21).…”

“…Large-scale applications are also possible to implement this approach, in contrast to the method of resource management based on dynamic planning [18]. In addition, in contrast to intelligent balancing methods [19][20][21], the proposed method minimizes latency and helps bring the process closer to real time. This is made possible by using the partitioning of the foggy environment into disjointed virtual clusters according to expression (1).…”

“…Methods of balancing computing resources in decentralized systems using neural networks are used in [19,20]. But these methods are unsuitable for systems operating under a mode close to real time.…”

Neural Network Support for Automation of Object Control With Multifactorial Input Under Conditions of Uncertainty

“…In these conditions, it is appropriate to apply intelligent data analysis procedures based on artificial neural networks [3][4][5][6][7][8]. This choice is due to the important pragmatic advantages of neural network technologies:…”