Early Forest Fire Detection using Texture Analysis of Principal Components from Multispectral Video

Davenport, Timothy M

doi:10.15368/theses.2012.112

Cited by 4 publications

(6 citation statements)

References 22 publications

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“…Tim Davenport's thesis [6] studies the spectral, temporal, and spatial attributes of a smoke plume. Davenport applies PCA to single instance multi-spectral images and to multiinstance, multi-spectral images.…”

Section: Discussionmentioning

confidence: 99%

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

Radjabi¹

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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.

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Section: Discussionmentioning

confidence: 99%

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

Radjabi¹

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“…Also, only the visible sensor was time stamped, a similar method was used to temporally align all the sensors. For more information on the fire test location, distance between the fire and the sensors, fuels that were burned, data format, time alignment, and field of view matching, please see [1], [3], [4].…”

Section: Preprocessingmentioning

confidence: 99%

“…% After Scaling to Full Dynamic Range % Display Histograms for Non adaptive before Full Dynamic Range Scaling figure(51) subplot(3,3,1) imhist(PC1_Scaled) subplot(3,3,2) imhist(PC2_Scaled) subplot(3,3,3) imhist(PC3_Scaled) subplot(3,3,4) imhist(PC4_Scaled) subplot(3,3,5) imhist(PC5_Scaled) subplot(3,3,6) …”

mentioning

confidence: 99%

Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components

Mr.¹,

Fouad²

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Forest fires constantly threaten ecological systems, infrastructure and human lives. The purpose behind this study is minimizing the devastating damage caused by forest fires. Since it is impossible to completely avoid their occurrences, it is essential to accomplish a fast and appropriate intervention to minimize their destructive consequences. The most traditional method for detecting forest fires is human based surveillance through lookout towers. However, this study presents a more modern technique. It utilizes land-based real-time multispectral video processing to identify and determine the possibility of fire occurring within the camera's field of view. The temporal, spectral, and spatial signatures of the fire are exploited. The methods discussed include: (1) Range filtering followed by entropy filtering of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively. Cooled Midwave Infrared (IR) camera is used to capture the heat distribution within the field of view. The fire core is then isolated using texture analysis techniques: first, range filtering applied on two consecutive IR frames, and then followed by entropy filtering of their absolute difference. Since smoke represents the earliest sign of fire, this study also explores multiple techniques for detecting smoke plumes in a given scene. The spatial and temporal variance of smoke plume is captured using temporal Principal Component Analysis, PCA. The results show that a smoke plume is readily segmented via PCA applied on the visible Blue band over 2 seconds sampled every 0.2 v seconds. The smoke plume exists in the 2 nd principal component, and is finally identified, segmented, and isolated, using either motion analysis or adaptive intensity threshold. Experimental results, obtained in this study, show that the proposed system can detect smoke effectively at a distance of approximately 832 meters with a low false-alarm rate and short reaction time. Applied, such system would achieve early forest fire detection minimizing fire damage.

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“…In 2012 Timothy Davenport proposed on texture analysis of data put through a principal component analysis (PCA) to detect smoke from a fire [10]. A PCA is performed on images from red, blue, green, mid wave infrared and long wave infrared frames.…”

Section: Research Preformed At Cal Polymentioning

confidence: 99%

“…The MWIR and LWIR data is combined in one raw file. These raw files were previously broken down into individual frames [10]. For the rest of this thesis all data generated from one raw file is considered a data set.…”

Section: Data Formatmentioning

confidence: 99%

Early Wildfire Detection Using Temporal Filtering and Multi-Band Infrared Analysis

Boynton¹

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Every year wildfires threaten or destroy ecological habitats, man-made infrastructure and people's lives. Additionally millions of dollars are spent each year trying to prevent and control these fires. Ideally if a wildfire can be detected before it rages out of control it can be extinguished and avoid large scale devastation. Traditional manned fire lookout towers are neither cost effective nor particularly efficient at detecting wildfire. It is proposed that temporal filtering can be used to isolate the signals created at the beginnings of potential wildfires. Temporal filtering can remove any background image and any periodic signals created by the camera movement. Once

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Early Forest Fire Detection using Texture Analysis of Principal Components from Multispectral Video

Cited by 4 publications

References 22 publications

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

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

Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components

Early Wildfire Detection Using Temporal Filtering and Multi-Band Infrared Analysis

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