A highly accurate and fast identification of forest fire based on supervised multi model image classification algorithm (smica)

In recent years, the forest area is gradually decreased because of increasing forest fire and human activities. The satellite sensor is used to collect the forest thermal image in different places and analyze the data in these images to detect the fire region if they occur. The proposed Supervised Multi-Model Image Classification Algorithm (SMICA) is used to analyze the data in forest image. In first, the input image is preprocessed to enhance the image quality, because the input image has the noise, so the preprocessing technique is used to eliminate the noise in this system and enhance the image quality. The preprocessed image is taking to the segmentation process; it processes the image to adjacent the forest sub-area. In this system, the affected area is separately detected, and it gives the accurate forest fire in this system because the output image intensity is better to stabilize the average value of the image. Also, this proposed Finite Image Clustering Segmentation (FICS) preserves to segment the image edges and decreasing the excessive noise and region of the output image, and it was determined to split the image into small sub-areas. The performance metrics of proposed classifier based accuracy, specificity, and sensitivity compared with the conventional model and analyzed the output of the proposed SMICA system.