Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data

The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for...

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Main Authors: Anesmar Olino de Albuquerque, Osmar Abílio de Carvalho Júnior, Osmar Luiz Ferreira de Carvalho, Pablo Pozzobon de Bem, Pedro Henrique Guimarães Ferreira, Rebeca dos Santos de Moura, Cristiano Rosa Silva, Roberto Arnaldo Trancoso Gomes, Renato Fontes Guimarães
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/13/2159
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author Anesmar Olino de Albuquerque
Osmar Abílio de Carvalho Júnior
Osmar Luiz Ferreira de Carvalho
Pablo Pozzobon de Bem
Pedro Henrique Guimarães Ferreira
Rebeca dos Santos de Moura
Cristiano Rosa Silva
Roberto Arnaldo Trancoso Gomes
Renato Fontes Guimarães
author_facet Anesmar Olino de Albuquerque
Osmar Abílio de Carvalho Júnior
Osmar Luiz Ferreira de Carvalho
Pablo Pozzobon de Bem
Pedro Henrique Guimarães Ferreira
Rebeca dos Santos de Moura
Cristiano Rosa Silva
Roberto Arnaldo Trancoso Gomes
Renato Fontes Guimarães
author_sort Anesmar Olino de Albuquerque
collection DOAJ
description The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil.
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spelling doaj.art-12dba8ca06d34720ac0beb82b7c679ec2023-11-20T05:58:46ZengMDPI AGRemote Sensing2072-42922020-07-011213215910.3390/rs12132159Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed DataAnesmar Olino de Albuquerque0Osmar Abílio de Carvalho Júnior1Osmar Luiz Ferreira de Carvalho2Pablo Pozzobon de Bem3Pedro Henrique Guimarães Ferreira4Rebeca dos Santos de Moura5Cristiano Rosa Silva6Roberto Arnaldo Trancoso Gomes7Renato Fontes Guimarães8Departamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Engenharia Elétrica, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Engenharia Elétrica, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilDepartamento de Geografia, Campus Universitário Darcy Ribeiro, Asa Norte, Universidade de Brasília, DF, Brasília 70910-900, BrazilThe center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil.https://www.mdpi.com/2072-4292/12/13/2159irrigationdeep learningU-netResUnetSharpMaskLandsat-8
spellingShingle Anesmar Olino de Albuquerque
Osmar Abílio de Carvalho Júnior
Osmar Luiz Ferreira de Carvalho
Pablo Pozzobon de Bem
Pedro Henrique Guimarães Ferreira
Rebeca dos Santos de Moura
Cristiano Rosa Silva
Roberto Arnaldo Trancoso Gomes
Renato Fontes Guimarães
Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
Remote Sensing
irrigation
deep learning
U-net
ResUnet
SharpMask
Landsat-8
title Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
title_full Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
title_fullStr Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
title_full_unstemmed Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
title_short Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
title_sort deep semantic segmentation of center pivot irrigation systems from remotely sensed data
topic irrigation
deep learning
U-net
ResUnet
SharpMask
Landsat-8
url https://www.mdpi.com/2072-4292/12/13/2159
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