Detection of industrial storage tanks at the city-level from optical satellite remote sensing images

Mingyuan Zhu, Zhibao Wang, Lu Bai, Jie Zhang, Jinhua Tao, Liangfu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)


Industrial storage tanks are widely used in petroleum, chemical industry, metallurgy and other process industries. The use of storage tanks are important in ensuring industrial safety production and product storage. Recently, concerns are rising over leaks and spills which leads to potential environmental and public health risks, for example, air pollution. Thus, there is a need to detect and monitor the tanks, which has become a regulatory requirement in most countries. The monitoring of industrial storage tanks in a city is of great practical significance to the planning and construction of the city. With the recent development of remote sensing technology, computer vision and image processing, it is possible to automatically detect industrial storage tanks from optical remote sensing images. This paper mainly studies the automatic detection of urban industrial storage tanks using deep learning based algorithm, aiming to help people manage and monitor the urban environment and resources. In this paper, we collected optical satellite remote sensing images of the city as a unit and created a city-level dataset including three cities of Guangdong province in south China by utilising satellite image data obtained from Google Earth imagery. To explore the effect of the deep learning object detection algorithm for the industrial storage tanks detection at the city-level, a deep learning model built with SSD (Single-Shot Detector) was trained based on our collected dataset. To further improve the detection accuracy of industrial storage tanks, Hough transform was used to reduce the false alarm in the deep learning results. The experimental results show that the combination of deep learning target detection model and Hough transform is effective in detecting of industrial storage tank on the collected city-level dataset and can achieve promising detection results.

Original languageEnglish
Title of host publicationProceedings Volume 11862, Image and Signal Processing for Remote Sensing XXVII; 118620Y (2021)
EditorsLorenzo Bruzzone, Francesca Bovolo, Jon Atli Benediktsson
Number of pages33
ISBN (Electronic)9781510645684
Publication statusPublished (in print/issue) - 12 Sept 2021
EventImage and Signal Processing for Remote Sensing XXVII - Online Only, Spain
Duration: 13 Sept 202118 Sept 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceImage and Signal Processing for Remote Sensing XXVII

Bibliographical note

Funding Information:
This work was supported in part by TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903.

Publisher Copyright:
© 2021 SPIE.


  • Deep learning
  • Hough transform
  • Industrial storage tanks
  • Remote sensing


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