Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data
| Autor: | M.A. Pavlova, V.A. Timofeev, D.A. Bocharov, D.S. Sidorchuk, A.L. Nurmukhametov, A.V. Nikonorov, M.S. Yarykina, I.A. Kunina, A.A. Smagina, M.A. Zagarev |
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| Jazyk: | English<br />Russian |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Компьютерная оптика, Vol 47, Iss 3, Pp 451-463 (2023) |
| Druh dokumentu: | article |
| ISSN: | 0134-2452 2412-6179 |
| DOI: | 10.18287/-6179-CO-1235 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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