Ms. Irina Vasendina Profile

Irina Vasendina

at Northern (Arctic) Federal Univ

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Publications (2)

Proceedings Article | 6 May 2021 Paper

Development of a methodology for determining overgrown agricultural fields based on data from unmanned aerial vehicles on computer vision

K. Shoshina, I. Vasendina, G. Volkova, T. Tyurbeeva

Proceedings Volume 11758, 117580V (2021) https://doi.org/10.1117/12.2588214

KEYWORDS: Agriculture, Unmanned aerial vehicles, Reliability, Image processing, Error analysis, Vegetation, Statistical analysis, Scientific research, Geographic information systems

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Agricultural land is a strategic government resource. Successful agribusiness depends on good land management. Therefore, it is important to take into account many points, including the degree of overgrowth of agricultural land. Overgrowing of fields leads to a decrease in the quality of soil fertility. Monitoring of geographical objects is most easily carried out with the help of geographic information systems and unmanned aerial vehicles. This reduces the time spent exploring the area, as well as improving the reliability of the results. In our case, this is the definition of overgrowth of agricultural fields. The developed methodology will be useful both for state agribusiness monitoring and control services and for landowners. This determines the relevance of scientific research. The work aims to develop a method based on computer vision or automated processing of images from unmanned aerial vehicles (UAVs) to determine the degree of overgrowth of agricultural fields. As a result, a conceptual model of the methodology was built, as well as an information system developed on its basis for thematic image processing. A snapshot of an agricultural plot of the Vologda region (Russia) is used as source material. An error matrix is used to evaluate the image's division into areas of agricultural fields and vegetation. The matrix is based on a comparison of the reference result of decoding with the result of using the method. The matrix allows us to take into account errors associated with the incorrect classification. The reliability assessment consists of two stages: the creation of a matrix, the dimension of which is determined by the number of classes; the calculation of statistical accuracy estimates in percentage terms, based on the results. Using the error matrix and Cohen's Kappa index, the reliability of the decoding was evaluated using the developed methodology. Compared to conventional decoding without preprocessing steps and trainable classification, the confidence increased by 11%.

Proceedings Article | 20 September 2020 Paper

Application of intelligent technologies for getting information about the state of natural resources when monitoring the ecosystems of the northern territories

A. Guryev, I. Vasendina, G. Volkova, T. Tyurbeeva

Proceedings Volume 11528, 1152814 (2020) https://doi.org/10.1117/12.2574463

KEYWORDS: Ecosystems, Data processing, Information technology, Neural networks, Image segmentation, Photography, Image processing, Climatology, Information operations, Spatial resolution

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Along with the growth of economic activity in the northern regions of Russia, there is an increasing need to assess their impact on the ecosystems of the northern territories. Due to the severe climatic conditions and low recovery capacity of northern ecosystems, it is important to quickly identify and respond to emerging negative processes. When monitoring terrestrial ecosystems, a key role is played by the availability of up-to-date, constantly updated information about the state of the northern ecosystem. One of the main sources of operational information is ultra-high spatial resolution images from unmanned aerial vehicles. Intelligent technologies and neural networks, in particular, are used to solve various point problems of thematic interpretation of spatial data on images, but are not used in combination because of the lack of elaboration of the mechanism for linking them together, and are also practically not focused on the problems of territory management. The article describes a formalized approach to combining a pool of intelligent technologies at two levels for the regular receipt of up-to-date information about ecosystem objects that enters the dynamic monitoring and management system of the northern territories. Within this approach, when obtaining information about the state of natural resources for monitoring northern ecosystems, the conceptual apparatus of different subject areas are taken into account: the area of territorial management tasks; natural resources of the ecosystem; remote sensing of the Earth. It is proposed to develop a mechanism for linking concepts from subject areas, their characteristics and dependencies, and building a semantic model, its formal description. The implementation of the process of obtaining information based on combining a sequence of neural networks of such modern architectures as U-Net and Res-Net is considered in this paper. The proposed approach provides a unified interaction of information manipulation processes based on various intelligent technologies for the regular receipt of up-to-date information about the state of natural resources in the framework of a dynamic monitoring and management system for the northern territories.

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