Intrusion detection on oil pipeline right of way using monogenic signal representation

Binu M. Nair; Varun Santhaseelan; Chen Cui; Vijayan K. Asari

doi:10.1117/12.2015640

23 May 2013 Intrusion detection on oil pipeline right of way using monogenic signal representation

Binu M. Nair, Varun Santhaseelan, Chen Cui, Vijayan K. Asari

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87451U (2013) https://doi.org/10.1117/12.2015640
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

We present an object detection algorithm to automatically detect and identify possible intrusions such as construction vehicles and equipment on the regions designated as the pipeline right-of-way (ROW) from high resolution aerial imagery. The pipeline industry has buried millions of miles of oil pipelines throughout the country and these regions are under constant threat of unauthorized construction activities. We propose a multi-stage framework which uses a pyramidal template matching scheme in the local phase domain by taking a single high resolution training image to classify a construction vehicle. The proposed detection algorithm makes use of the monogenic signal representation to extract the local phase information. Computing the monogenic signal from a two dimensional object region enables us to separate out the local phase information (structural details) from the local energy (contrast) thereby achieving illumination invariance. The first stage involves the local phase based template matching using only a single high resolution training image in a local region at multiple scales. Then, using the local phase histogram matching, the orientation of the detected region is determined and a voting scheme gives a certain weightage to the resulting clusters. The final stage involves the selection of clusters based on the number of votes attained and using the histogram of oriented phase feature descriptor, the object is located at the correct orientation and scale. The algorithm is successfully tested on four different datasets containing imagery with varying image resolution and object orientation.

Citation Download Citation

Binu M. Nair, Varun Santhaseelan, Chen Cui, and Vijayan K. Asari "Intrusion detection on oil pipeline right of way using monogenic signal representation", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451U (23 May 2013); https://doi.org/10.1117/12.2015640

ACCESS THE FULL ARTICLE

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