Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

Saraiva, Marciano; Protas, Églen; Salgado, Moisés; Souza, Carlos M.

doi:10.3390/rs12030558

Cited by 62 publications

(42 citation statements)

References 18 publications

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“…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.…”

Section: Related Work On Center Pivot Detectionmentioning

confidence: 99%

“…Subsequently, two articles report the use of semantic segmentation for the detection 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.…”

Section: Related Work On Center Pivot Detectionmentioning

confidence: 99%

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

et al. 2020

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Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels.

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Section: Related Work On Center Pivot Detectionmentioning

confidence: 99%

Section: Related Work On Center Pivot Detectionmentioning

confidence: 99%

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

et al. 2020

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“…Machine learning methods have gained strength with these new resources. Deep learning methods have obtained state-of-the-art results in many Earth observation applications, such as image classification [19], semantic segmentation [20], phenological studies [21], poverty mapping [22], precision agriculture [23], and detection of pivot irrigation systems with very-high spatial and temporal resolution imagery [24]. An extensive survey on deep-learning-driven remote sensing image scene understanding can be found in [25].…”

Section: Related Workmentioning

confidence: 99%

Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

Balaniuk

Isupova

Reece

2020

Sensors

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This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.

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“…They achieved high precision (95.85%) and recall (93.33%) accuracies, but the method was time-consuming since it relied on a sliding window applied to the test images. In Brazil, Saraiva [30] used U-Net [31] to detect and map center pivot irrigation systems with a high precision of 99% and a relatively low recall of 88%. Similarly, Albuquerque [32] used U-Net semantic segmentation to map center pivot irrigation systems in three Brazilian study sites with an improved recall of 94.57% and a precision of 98.26%.…”

Section: Introductionmentioning

confidence: 99%

Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Tang

Arvor

Corpetti

et al. 2021

Water

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Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.

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Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

Cited by 62 publications

References 18 publications

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

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

Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

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