Version 1
: Received: 10 December 2021 / Approved: 14 December 2021 / Online: 14 December 2021 (15:01:03 CET)
How to cite:
Pavlova, M.; Timofeev, V.; Bocharov, D.; Kunina, I.; Smagina, A.; Zagarev, M. Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data. Preprints2021, 2021120243 (doi: 10.20944/preprints202112.0243.v1).
Pavlova, M.; Timofeev, V.; Bocharov, D.; Kunina, I.; Smagina, A.; Zagarev, M. Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data. Preprints 2021, 2021120243 (doi: 10.20944/preprints202112.0243.v1).
Cite as:
Pavlova, M.; Timofeev, V.; Bocharov, D.; Kunina, I.; Smagina, A.; Zagarev, M. Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data. Preprints2021, 2021120243 (doi: 10.20944/preprints202112.0243.v1).
Pavlova, M.; Timofeev, V.; Bocharov, D.; Kunina, I.; Smagina, A.; Zagarev, M. Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data. Preprints 2021, 2021120243 (doi: 10.20944/preprints202112.0243.v1).
Abstract
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.
Keywords
digital farming; remote sensing; land management; multispectral image processing; land cover mapping; agricultural field boundary
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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