Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering

The rapidly spreading forest fire is always uncontrollable and unpredictable and also has a disastrous effect on the environment as well as human individuals. This is also able to wipe out large acres of forest as well as the agricultural lands. Therefore, in this paper, a fire detection approach is presented in the nonsubsampled contourlet (NSCT) domain by extracting the fused fire regions of visible and infrared (IR) images using spatial fuzzy C-means clustering (SpFCM). Firstly, the NSCT is applied to decompose the source visible and IR images into one low and several high-frequency components. Low-frequency NSCT component is fused using a pulse coupled neural network (PCNN) motivated by the sum-modified Laplacian to retain the maximum information available in both the source images. Local log Gabor energy based fusion rule is employed to fuse the high-frequency NSCT components that are able to preserve the maximum detail information. Later, the fused image is reconstructed by applying the inverse NSCT. Finally, all the fire pixels are identified in the fused images and segmented using the fuzzy clustering approach having spatial information also. Furthermore, several experiments are conducted to evaluate the fire detection ability of the proposed framework in terms of visual appearance as well as several performance evaluation parameters. Experimental results show the superiority of the proposed approach over the other existing fusion approaches by improving all the performance parameters.