Wildfire Detection System Based on Principal Component Analysis and Image Processing of Remote-Sensed Video

Abstract

Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region.The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques.The real-world deployment and usability are described for this system.