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.

中文翻译：

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中心枢轴灌溉系统的遥感数据深度语义分割

中心枢纽灌溉系统（CPIS）是一种现代灌溉技术，由于与传统灌溉方法相比，它的耗水效率高且劳动力少，因此广泛用于精密农业。CPIS是巴西机械化灌溉领域的领导者，并预测未来几年的增长。因此，中心枢纽区域的测绘是估算农业产量，确保粮食安全，水资源管理和环境保护的战略因素。在这方面，卫星图像的数字处理是主要的工具，可以低成本和敏捷性进行区域和连续监测。然而，使用遥感图像自动检测CPIS仍然是一个挑战，许多研究已采用视觉解释。尽管CPIS在景观中呈现出一致的圆形形状，但是这些区域在不同的人工林中会随时间变化而具有很高的内部变化，而仅凭光谱特性很难做到这一点。使用卷积神经网络（CNN）进行深度学习是一种新兴的方法，它引发了图像分割的一场革命，超越了传统方法，并实现了更高的准确性和效率。这项研究旨在评估使用Landsat-8表面反射图像（七个波段）的基于CNN的算法对CPIS的深度语义分割的使用。所开发的方法可细分为以下步骤：（a）确定巴西中部CPIS高度集中的三个研究区域；（b）考虑降雨和干旱时期的季节性变化，获取Landsat-8影像；（c）定义包含Landsat图像和256×256像素地面真伪的CPIS数据集；（d）使用三种CNN架构（U-net，Deep ResUnet和SharpMask）进行培训；（e）准确性分析；（f）使用六个跨步值（8、16、32、64、128和256）进行大图像重建。三种方法均达到了最新的结果，U-net的普及率略高于Deep ResUnet和SharpMask（系数分别为0.96、0.95和0.92 Kappa）。这项研究的新颖之处在于大图像重建中的重叠像素分析。较低的步幅值具有通过接收器工作特性曲线（ROC曲线）和Kappa量化的改进，并且在帧边缘的错误也较少。重叠的图像显着提高了准确性，并减少了分类帧边缘中出现的误差。此外，我们在旱季开始时获得了更高的准确性结果。本研究使得能够建立中心枢轴图像的数据库以及用于绘制巴西中部中心枢轴的适当方法。