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Oil Tank Detection from Remote Sensing Images based on Deep Convolutional Neural Network |
Yingjie Wang(),Qiao Zhang(),Yanmei Zhang,Yin Meng,Wen Guo |
The Third Remote Sensing Geomatics Institute of National Administration of Surveying, Mapping and Geoinformation, Chengdu 610100, China |
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Abstract Oil tanks are industrial facilities for storing oil products, which are commonly used in industrial parks such as oil refineries. The rapid detection of oil tank target through satellite or aerial remote sensing images can quickly find suspected industrial parks, providing scientific and technical support for natural resource regulation and ecological environment protection. This paper discussed the possibility of object detection with high-resolution remote sensing images based on deep convolutional neural network. The state-of-the-art algorithms of Faster R-CNN (Convolutional Neural Network) and R-FCN (Region-based Fully Convolutional Network) and three network models were applied for oil tank detection from high-resolution remote sensing images. To promote the detection accuracy and execution efficiency for the oil tank target, an improved approach by increasing the scales of the anchor was proposed. The optimum recall reached about 80%. The results confirm that deep learning network approach can rapid detect oil tank from high-resolution remote sensing image. This provide an example and new idea for rapid detection small target from remote sensing image by deep learning technology.
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Received: 03 August 2018
Published: 16 October 2019
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Corresponding Authors:
Qiao Zhang
E-mail: 641055472@qq.com;scrs_qiaozh@163.com
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