Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles View Full Text


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Article Info

DATE

2017-01-05

AUTHORS

Chi Yuan, Zhixiang Liu, Youmin Zhang

ABSTRACT

Due to their fast response capability, low cost and without danger to personnel safety since there is no human pilot on-board, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring and detecting forest fires. This paper proposes a novel forest fire detection method using both color and motion features for processing images captured from the camera mounted on a UAV which is moving during the whole mission period. First, a color-based fire detection algorithm with light computational demand is designed to extract fire-colored pixels as fire candidate regions by making use of chromatic feature of fire and obtaining fire candidate regions for further analysis. As the pose variations and low-frequency vibrations of UAV cause all objects and background in the images are moving, it is challenging to identify fires defending on a single motion based method. Two types of optical flow algorithms, a classical optical flow algorithm and an optimal mass transport optical flow algorithm, are then combined to compute motion vectors of the fire candidate regions. Fires are thereby expected to be distinguished from other fire analogues based on their motion features. Several groups of experiments are conducted to validate that the proposed method can effectively extract and track fire pixels in aerial video sequences. The good performance is anticipated to significantly improve the accuracy of forest fire detection and reduce false alarm rates without increasing much computation efforts. More... »

PAGES

635-654

References to SciGraph publications

  • 2003-06. Survey of Intelligent Control Techniques for Humanoid Robots in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 1992. Sliding Modes in Control and Optimization in NONE
  • 2013-10-18. A Distributed Deployment Strategy for Multi-Agent Systems Subject to Health Degradation and Communication Delays in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 2011-08-16. An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 2010-12-08. Experimental Results in Multi-UAV Coordination for Disaster Management and Civil Security Applications in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 2015-10-22. A Learning-Based Fault Tolerant Tracking Control of an Unmanned Quadrotor Helicopter in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 2010. A Hybrid Moving Object Detection Method for Aerial Images in ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2010
  • 2000-06. Real-Time Feature Matching in Image Sequences for Non-Structured Environments. Applications to Vehicle Guidance in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 2013-11-06. Early Warning System of Forest Fire Detection Based on Video Technology in PROCEEDINGS OF THE 9TH INTERNATIONAL SYMPOSIUM ON LINEAR DRIVES FOR INDUSTRY APPLICATIONS, VOLUME 3
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    http://scigraph.springernature.com/pub.10.1007/s10846-016-0464-7

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    DIMENSIONS

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    195 rdf:type schema:Organization
     




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