Prof. Valdislav V. Sergeyev Profile

Prof. Valdislav V. Sergeyev

at Samara National Research Univ.

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Publications (2)

Proceedings Article | 27 June 2019 Paper

Plant species concentration estimation using multispectral remote sensing images

Anna Denisova, Anna Egorova, Vladislav Sergeyev

Proceedings Volume 11174, 111740B (2019) https://doi.org/10.1117/12.2531806

KEYWORDS: Vegetation, Image segmentation, Remote sensing, Image classification, Image processing algorithms and systems, Feature extraction, Sensors, Error analysis, Near infrared, Multispectral imaging

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Special forensic examinations of vegetation areas become an important part of the practice preceding to different land rearrangement activities for the purposes of environmental conservation and rational land use. A traditional approach to such examinations includes laborious ground surveys and produces approximate damage estimations for the large sites. Fortunately, remote sensing (RS) data delivers impressive opportunities to simplify the examinations and increase their accuracy. Thus, the substitution of the traditional ground-survey based methods with RS-data oriented technologies is an important problem. In this paper, we propose a method for plant species concentration (PSC) estimation using RS images that can be applied in special forensic examinations of vegetation areas. PSC is one of the key factors indicating vegetation area status. PSC describes a fraction of the land area covered by the plants of particular species. For example, a tree concentration in the agricultural fields indicates the time elapsed from the last processing date and may be useful for abandoned-field determination. The proposed method assumes that the examined area contains a finite number of target vegetation classes. The expert puts several points in the image for each class without exhaustive border delineation to define typical class representatives. Then, the superpixel segmentation and feature extraction algorithm with further kmeans clustering are used to get the entire study-area classification. Finally, PSC is computed as the elementary vegetation class concentration. Keeping in mind abandoned-field determination, we evaluated our method with simulated and real RS images containing four classes: sparse grass, low grass, high grass, and trees. We found that shadows should be defined as a separate class to minimize estimation errors in real images. Moreover, the superpixel segmentation increases the PSC accuracy by 28% with respect to simple per-pixel clustering. Thus, the experimental results proved the applicability of our method for PSC estimation.

Proceedings Article | 9 August 2018 Paper

EM clustering algorithm modification using multivariate hierarchical histogram in the case of undefined cluster number

A. Y. Denisova, V. V. Sergeyev

Proceedings Volume 10806, 108064H (2018) https://doi.org/10.1117/12.2503151

KEYWORDS: Expectation maximization algorithms, Image processing, Algorithm development, Image processing algorithms and systems

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