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

Abstract

<jats:p>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.</jats:p>

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

“…The different colors, textures, and spectral information inside and between the center pivots make it challenging to obtain accurate classifications by traditional machine learning methods based on pixel or vegetation indices. Consistent automatic detection of center pivots emerges with methods based on deep learning [85,103,104]. Zhang et al [103] were the precursors in using CNNs for automatic identification of CPIS.…”

“…Saraiva et al [104] perform the segmentation of the U-Net architecture of the images of the PlanetScope constellation containing four channels (blue, green, red, and near-infrared). De Albuquerque et al [85] compare three CNN architectures (U-net, Deep ResUnet, and SharpMask) and use Landsat-8 surface reflectance images composed of 7 bands in the rainy and dry period. In this context, instance segmentation is still an unexplored method for this target, which is a differential for the management of irrigated areas, as it establishes the quantity and size of the central pivots, which are fundamental factors for forecasting the harvest and water consumption.…”

“…Some open challenges, such as SpaceNet [108] (which provides polygons), could use the same methodology used in this paper to experiment instance segmentation algorithms. Nevertheless, we used the CPIS database developed by Albuquerque et al [85] based on the survey of center pivots in Brazilian territory by the National Water Agency (ANA) in 2014 [100], since it presents high relevance to agricultural studies. The elaboration of the ANA dataset used visual interpretation on a computer screen.…”

“…When the stride is smaller than the window size, it creates an overlap between consecutive frames. Semantic and instance segmentation errors occur predominantly at the frame edges, corrected, or minimized with overlapping images [85,87].…”

“…This is a data limitation for optical Earth observation sensors that are generally multispectral, where the available channels provide complementary information that maximizes accuracy. In semantic segmentation, approaches to aggregate more information considered: (a) the use of image fusion techniques, where the three bands used are data integration products [84]; (b) input layer adequation to support a larger amount of channels, e.g., 14 channels [15] 12 channels [14], 7 channels [85], and 4 channels [86].…”