Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images

Zhang, Libao; Liu, Congyang

doi:10.1109/icip.2019.8803122

Cited by 3 publications

(1 citation statement)

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“…Good recognition results had been achieved on extracting the spatial position information of the South China Sea oil and gas drilling platform by using the time series Landsat-8 Operational Land Imager (OLI) image and layered screening strategy. In recent years, most of the research interests are among the area of remote monitoring of the existing oil tank detection [14] from satellite images. In [15], a traditional machine learning algorithm with the Speeded up Robust Features (SURF) technique and Support Vector Machine (SVM) classifier has been used for oil tank detection.…”

Section: Related Work 121 Oil-related Monitoring Using Remote Sensing...mentioning

confidence: 99%

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

et al. 2021

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Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

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Section: Related Work 121 Oil-related Monitoring Using Remote Sensing...mentioning

confidence: 99%

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

et al. 2021

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End-to-End Method With Transformer for 3-D Detection of Oil Tank From Single SAR Image

Zhang

Guo

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images

Zhang

et al. 2022

Remote Sensing

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Oil storage tank detection and classification in synthetic aperture radar (SAR) images play a vital role in monitoring energy distribution and consumption. Due to the SAR side-looking imaging geometry and multibouncing scattering mechanism, dense oil tank detection and classification tasks have faced more challenges, such as overlapping, blurred contours, and geometric distortion, especially for small-sized tanks. To address the above issues, this paper proposes YOLOX-TR, an improved YOLOX based on the Transformer encoder and structural reparameterized VGG-like (RepVGG) blocks, to achieve end-to-end oil tank detection and classification in densely arranged areas of large-scale SAR images. Based on YOLOX, the Transformer encoder, a self-attention-based architecture, is integrated to enhance the representation of feature maps and capture the region of interest of oil tanks in densely distributed scenarios. Furthermore, RepVGG blocks are employed to reparameterize the backbone with multibranch typologies to strengthen the distinguishable feature extraction of multi-scale oil tanks without increasing computation in inference time. Eventually, comprehensive experiments based on a Gaofen-3 1 m oil tank dataset (OTD) demonstrated the effectiveness of the Transformer encoder and RepVGG blocks, as well as the performance superiority of YOLOX-TR with a mAP and mAP0.5 of 60.8% and 94.8%, respectively.

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Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images

Cited by 3 publications

References 20 publications

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

End-to-End Method With Transformer for 3-D Detection of Oil Tank From Single SAR Image

Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images

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