Paddock segmentation using multi-temporal satellite imagery

North, Heather; Pairman, D.; Belliss, S.E.

doi:10.1109/igarss.2014.6945951

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…В работах, посвященных оконтуриванию полей, использовались данные различных спутников: SPOT [31,39], LANDSAT [32], WorldWide-2/3 [21,34], Sentinel-2 [20,29,35] Анализ экспериментальных разделов других работ показал, что точное сравнение подходов затруднено, в основном из-за недоступности выборок данных, используемых в этих работах. В работах [1,27,29,32,34,35] разметка получена вручную и недоступна; в [33] не описано происхождение правильных ответов для оценки алгоритма; в [20,21,26,35]…”

Section: исторические данныеunclassified

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2023

Computer Optics

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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.

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Section: исторические данныеunclassified

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

Pavlova¹,

Timofeev²,

Bocharov³

et al. 2023

Computer Optics

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“…In these studies, research data were provided by various satellites such as SPOT [54,61], LANDSAT [55], WorldWide-2/3 [45,57], or Sentinel-2 [4,52,58]. After analyzing the related work, we came to the conclusion that it is advisable to use a hybrid approach to field segmentation, taking into account historical data.…”

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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“…precise algorithm used depends on the image type, its content, and which features are of interest. The development of our method (our early work is described in [23]) has been guided by our observation of several key characteristics of the agricultural landscape, as seen in the imagery.…”

mentioning

confidence: 99%

Boundary Delineation of Agricultural Fields in Multitemporal Satellite Imagery

North

Pairman

Belliss

2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Agricultural land-use statistics are more informative per-field than per-pixel. Land-use classification requires up-to-date field boundary maps potentially covering large areas containing thousands of farms. This kind of map is usually difficult to obtain. We have developed a new, automated method for deriving closed polygons around fields from time-series satellite imagery. We have been using this method operationally in New Zealand to map whole districts using imagery from several satellite sensors, with little need to vary parameters. Our method looks for boundarieseither step edges or linear features-surrounding regions of low variability throughout the time series. Local standard deviations from all image dates are combined, and the result is convolved with a series of extended directional edge filters. We propose that edge linearity over a long distance is a more important criterion than spectral difference for separating fields, so edge responses are thresholded primarily by length rather than strength. The resulting raster edge map (combined from all directions) is converted to vector (GIS) format and the final polygon topology is built. The method successfully segments parcels containing different crops and pasture, as well as those separated by boundaries such as roads and hedgerows. Here we describe the technique and demonstrate it for an agricultural study site (4000 km 2) using SPOT satellite imagery. We show that our result compares favorably with that from existing segmentation methods in terms of both quantitative quality metrics and suitability for land-use classification.

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Paddock segmentation using multi-temporal satellite imagery

Cited by 4 publications

References 6 publications

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

Low-parameter method for delineation of agricultural fields in satellite images based on multi-temporal MSAVI2 data

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

Boundary Delineation of Agricultural Fields in Multitemporal Satellite Imagery

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