Oil Rig Recognition Using Convolutional Neural Network on Sentinel-1 SAR Images

Falqueto, Leonan E.; Sa, Jose A. S.; Paes, Rafael Lemos; Passaro, Angelo

doi:10.1109/lgrs.2019.2894845

Cited by 27 publications

(39 citation statements)

References 13 publications

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“…Following the methodology employed in [26], the original amplitude-type images were transformed into sigma-zero (dB) images. Figure 4 presents an optical image and its respective SAR image for the following targets: (i) Floating Production Storage and Offloading (FPSO) platforms P-48; (ii) Floating and Production Unit (FPU) P-53; (iii) Tension Leg Wellhead Platform (TLWP) P-61; (iv) Fixed Platform (FIX) PCH-1; and (v) Semisubmersible (SS) P-65.…”

Section: Extra Wide Swath Modementioning

confidence: 99%

“…In particular, we aim to classify oil rigs and ships in the Campos Basin, on the coast of Rio de Janeiro and Espírito Santo, Brazil, considering vertical-horizontal (VH) and vertical-vertical (VV) polarimetric images. For that, we considered the following methods: (M1) reproduction of results already presented in the literature, specifically the ones discussed in [26], which, to the best of our knowledge, is the only study related to the Campos Basin data set; (M2) evaluation of the sensitivity of the classifiers; (M3) expansion of the training data set concatenation considering the whole of the VH and VV polarization samples; (M4) expansion of the training data set concatenating half of the VH and VV polarization samples; and (M5) combining classifiers to obtain better accuracy results (a technique named stacked generalization).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.

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Section: Extra Wide Swath Modementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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“…However, there are other advanced techniques such as Machine Learning algorithms. With the wide application of Convolutional Neural Networks (CNNs) in ship target detection on the sea [7][8][9][10], this technology has been applied to oil and gas platform recognition [11].…”

Section: Introductionmentioning

confidence: 99%

Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

et al. 2021

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Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their spatial and temporal distribution, state attributes and other information directly reflect the exploitation level of regional oil and gas resources. Therefore, it is very important to build an automatic detecting method for offshore drilling rigs with good performance to accurately capture the temporal and spatial distribution and state of oil and gas exploitation activities. At present, there are two main groups of methods for offshore drilling rigs: invariant feature-based methods and nighttime firelight-based methods. Methods based on invariant location are more subjective in terms of their parameter settings and require intensive computation. Nighttime light-based methods are largely unable to identify offshore drilling rigs without associated waste gas ignition. Furthermore, multiple offshore drilling rigs in close proximity to one another cannot be effectively distinguished with low spatial resolution imagery. To address these shortcomings, we propose a new method for the automatic identification of offshore drilling rigs based on Landsat-7 ETM+ images from 2018 to 2019, taking the Caspian Sea as the research area. We build a nominal annual cloud and cloud shadow-free Normalized Difference Water Index (NDWI) composite by designing an optimal NDWI compositing method based of the influence of cloud and cloud shadow on the NDWI values of water, bare land (island) and offshore drilling rigs. The classification of these objects is simultaneously done during the compositing process, with the following rules: water body (Max_NDWI > 0.55), bare land (island) (Min_NDWI < −0.05) and offshore drilling rig (0 < Mean_NDWI < 0.4). A threshold segmentation and postprocessing were carried out to further refine the results. Using this method, 497 offshore platforms were automatically identified using a nominal annual cloud and cloud shadow-free NDWI composite image and Google Earth Engine. Validation using Sentinel-2 Multispectral Imager (MSI) and Google Earth images demonstrated that the correct rate of offshore drilling rig detection in the Caspian Sea is 90.2%, the missing judgment rate is 5.3% and the wrong judgment rate is 4.5%, proving the performance of the proposed method. This method can be used to identify offshore drilling rigs within a large water surface area relatively quickly, which is of great significance for exploring the exploitation status of offshore oil and gas resources. It can also be extended to finer spatial resolution optical remote sensing images; thus small-size drilling rigs can be effectively detected.

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“…29,30 However, the accuracy of the extraction result is limited owing to the local receiving field and short-term context information. 31,32 Some encoder-decoder networks are used to gradually restore edge details, [33][34][35][36] such as UNet, 37 SegNet, 38 and DeepLabv3. 39 Despite their success, some details are lost after the encoder downsampling, which means that predictions are often less accurate near the boundaries.…”

Section: Introductionmentioning

confidence: 99%

Improved semantic segmentation method using edge features for winter wheat spatial distribution extraction from Gaofen-2 images

Yin

Zhang

Han³

et al. 2021

J. Appl. Rem. Sens.

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In the final feature map obtained using a convolutional neural network for remote sensing image segmentation, there are great differences between the feature values of the pixels near the edge of the block and those inside the block; ensuring consistency between these feature values is the key to improving the accuracy of segmentation results. The proposed model uses an edge feature branch and a semantic feature branch called the edge assistant feature network (EFNet). The EFNET model consists of one semantic branch, one edge branch, one shared decoder, and one classifier. The semantic branch extracts semantic features from remote sensing images, whereas the edge branch extracts edge features from remote sensing images and edge images. In addition, the two branches extract five-level features through five sets of feature extraction units. The shared decoder sets up five levels of shared decoding units, which are used to further integrate edge features and deep semantic features. This strategy can reduce the feature differences between the edge pixels and the inner pixels of the object, obtaining a per-pixel feature vector with high inter-class differentiation and intra-class consistency. Softmax is used as the classifier to generate the final segmentation result. We selected a representative winter wheat region in China (Feicheng City) as the study area and established a dataset for experiments. The comparison experiment included three original models and two models modified by adding edge features: SegNet, UNet, and ERFNet, and edge-UNet and edge-ERFNet, respectively. EFNet's recall (91.01%), intersection over union (81.39%), and F1-Score (91.68%) were superior to those of the other methods. The results clearly show that EFNET improves the accuracy of winter wheat extraction from remote sensing images. This is an important basis not only for crop monitoring, yield estimation, and disaster assessment but also for calculating land carrying capacity and analyzing the comprehensive production capacity of agricultural resources.

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Oil Rig Recognition Using Convolutional Neural Network on Sentinel-1 SAR Images

Cited by 27 publications

References 13 publications

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

Improved semantic segmentation method using edge features for winter wheat spatial distribution extraction from Gaofen-2 images

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