Spectral texture classification of high-resolution satellite images for the state forest inventory in Russia

Дмитриев, Е. В.; Sokolov, Anton; Kozoderov, V. V.; Delbarre, Hervé; Мельник, П. Г.; Донской, С А

doi:10.1117/12.2532965

Cited by 2 publications

(1 citation statement)

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“…With regard to working with multispectral images, methods for use in their visualization [36], restoration [37,38], and correction [29] are being actively developed. As for information retrieval from satellite data, specific problem-oriented methods are being investigated, such as cropland boundaries segmentation [39], crops [40] and tree species classification [41,42], texture classification for forests inventory purposes [43], and windthrow detection [44].…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2021

Preprint

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This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

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