Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data
Автор: Pavlova Mariia Aleksandrovna, Timofeev Valerii Andreevich, Bocharov Dmitrii Aleksandrovich, Sidorchuk Dmitrii Sergeevich, Nurmukhametov Almir Linarovich, Nikonorov Artem Vladimirovi, Yarykina Mariya Sergeevna, Kunina Irina Andreevna, Smagina Anna Aleksandrovna, Zagarev Mikhail Aleksandrovich
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений, распознавание образов
Статья в выпуске: 3 т.47, 2023 года.
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This paper considers an issue of delineating agricultural fields in satellite images. In this task we follow a multi-temporal data approach. We show that on such data, good quality can be achieved using a simple low-parameter method. The method consists of a combination of a field detector and an edge detector. The field detection is based on an Otsu thresholding technique and for the edge detection we use a Canny detector. Facing a lack of available datasets and aiming to estimate the proposed algorithm, we prepared and published our dataset consisting of 18,859 expertly annotated fields using Sentinel-2 data. We implement one of the state-of-the-art deep-learning approaches and compare it with the proposed method on our dataset. The experiment shows the proposed simple multi-temporal algorithm to outperform the state-of-the-art instant data approach. This result confirms the importance of using multi-temporal data and for the first time demonstrates that the delineation problem can be solved at a lower cost without loss of quality. Our approach requires a significantly less amount of training data when compared with the NN-based one. The dataset of agricultural fields used in the work and the proposed algorithm implementation in Python are published in open access.
Low-parameter algorithm, computer vision, fields delineation, remote sensing, multi-temporal data, open dataset
Короткий адрес: https://sciup.org/140300065
IDR: 140300065 | DOI: 10.18287/2412-6179-CO-1235