The work is aimed at developing a model of an information system for the analysis and monitoring of remote sensing data by the example of processing hyper- and multispectral satellite images, which are widely used to analyze the state of static and dynamic objects in the Arctic region of the Russian Federation. For automatic analysis and decryption of Arctic data in the development of the model, methods of high-performance computing, radiometric calibration, filtering and clustering of images, as well as intelligent data processing methods using deep learning convolutional neural networks were used. Object-oriented design and united modeling language notation were used to develop the model. A data-level model, a conceptual model of the structure of system modules, including a resource storage center, a resource and results management center, and a presentation-level interface have been developed. To develop a diagram of the use cases of the information system, the structure of actors, use cases and their interrelations were identified. The logical model of the information system was created based on a class diagram consisting of the Resource and Results Manager Center, Intellectual Information System, Functional Neural Modules packages. The practical significance of the study is due to the fact that the results obtained will allow the development of a prototype of an information system that can be used for effective monitoring of “useful data" of the Arctic region of the Russian Federation, as well as to automate the processes of analysis, updating, storage and processing of data from objects in various areas of the Arctic infrastructure.
Technology for developing a prototype of an information system for monitoring remote sensing data for the arctic region
