Date of Award


Degree Name

MS in Electrical Engineering


Electrical Engineering


John Saghri


The aim of this study is to incorporate the spectral, temporal and spatial attributes of a smoke plume for Early Forest Fire Detection. Image processing techniques are used on multispectral (red, green, blue, mid-wave infrared, and long-wave infrared) video to segment and indentify the presence of a smoke plume within a scene.

The temporal and spectral variance of a smoke plume is captured through Principal Component Analysis (PCA) where the Multispectral-Multitemporal PCA is performed on a sequence of video frames simultaneously. The presence of a plume existing in one of the higher order principal components is determined by the texture of its spatial content. The texture is characterized by statistical descriptors derived from the principal component‟s joint probability density distribution of intensities occurring within a spatial relationship, known as a Gray Level Co-Occurrence Matrix (GLCM). Initial analysis is performed on selected frames where only a subset of time is considered. Once the parameters are chosen from the static analysis, the algorithms are executed on video through time to validate the method.

The results show that a smoke plume is readily segmented via PCA. Based on the five spectral bands over 3 seconds sampled at 1 second, the plume exists in the 7th principal component. Within these principal components, the smoke‟s presence is best identified by the correlation texture descriptor. The smoke is very spatially correlated compared to the scene at large. Therefore a spike in the spatial correlation of the principal components is all that is needed to identify the start of the smoke plume.

PC_5bands3at60s.avi (6174 kB)
PCs of 3 samples of red, green, blue, mwir, and lwir sampled at

Visible_PC7_GLCMCorrelation_Detection.avi (19331 kB)
Visible, PC7 and GLCM Correlation showing detection