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

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.

Development of Neural Network Models for Automatic Monitoring of Agricultural Land Degradation Based on Satellite Imagery

“…Однако для того, чтобы эта информация была актуальной, она нуждается в периодическом обновлении, что актуализирует проблему распознавания границ сельскохозяйственных полей. Обычно процесс разметки земель со спутниковых снимков выполняется вручную, но данный метод является неэффективным и трудоемким, особенно на больших территориях, таких как округа или регионы [8].…”