Mr. Guangfu Song Profile

Guangfu Song

at Northeast Petroleum Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 20 September 2020 Presentation + Paper

Detection of oil wells based on faster R-CNN in optical satellite remote sensing images

Guanfu Song, Zhibao Wang, Lu Bai, Jie Zhang, Liangfu Chen

Proceedings Volume 11533, 115330J (2020) https://doi.org/10.1117/12.2572996

KEYWORDS: Remote sensing, Satellite imaging, Satellites, Earth observing sensors, Machine learning, Data modeling, Renewable energy, Environmental monitoring, Environmental sensing, Image resolution

Read Abstract +

The number and location of oil wells represent the status of oilfield development, which is important for policyholders considering their impact on energy resources planning. More importantly, petroleum production has a potential risk on the environment and public health due to its impact associated with local soil and water. With the advancement of satellite remote sensing and computer vision, there is emerging research interest in the area of object detection using optical remote sensing images. The detection of oil wells from remote sensing images remains an unexplored research area. Therefore, automatic detection of oil wells is explored in this paper and aims to help the policyholders with resources planning and environment monitoring. CNN (Convolutional Neural Network) based deep learning methods are able to learn distinctive high-level features efficiently, which address the challenges in the object detection in remote sensing. In this paper, we explore frameworks to automatically detect oil wells from the optical remote sensing images based on Faster R-CNN (Regional Convolutional Neural Network). In order to evaluate our methods, we have built a dataset of oil wells named NEPU-OWOD V1.0 (Northeast Petroleum University - Oil Well Object Detection Version 1.0) based on high-resolution remote sensing images from Google Earth Imagery. The experimental results show high precision up to 92.4%, which demonstrate that our methods can detect the oil wells from remote sensing images effectively.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years