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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. v ACKNOWLEDGMENTS I would like to thank Dr. Saghri and Dr. Jacobs for proposing this noble and exciting project. I would like to thank them for their commitment and patience with my thesis. I'd also like to thank Raytheon for their sponsorship of the EFFD project and the Goleta fire experiments. I'd like to thank my friend Dennis for continuously asking, " How goes your thesis? " Lastly, I would like to thank my family for their love, encouragement, support, and patience. Thank you Mom and Dad for caring so deeply that I finish my research. Thank you Emily for your love, your care, and your support.

WILDFIRE DETECTION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS AND IMAGE PROCESSING OF REMOTE-SENSED VIDEO