Fire detection in nano-satellite imagery using Mask R-CNN Online publication date: Mon, 15-Jul-2024
by Aditi Jahagirdar; Neha Sathe; Sneh Thorat; Saloni Saxena
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 13, No. 1, 2024
Abstract: Increasing availability of satellite imagery has made it possible to detect forest fires through satellite imagery. This research aims at investigating early forest detection approaches using deep learning and satellite image segmentation. The algorithms implemented in this work are mask region-based convolutional neural network (Mask R-CNN), UNet and deep residual U-NET (ResUNet). The experimentation is carried out on publically available satellite image data having challenges like the presence of clouds, snow, rivers and sand, which gets confused with the smoke from the fire. The methods implemented here can successfully distinguish between these natural entities and the smoke emitted from the fire. It is seen that Mask R-CNN has an IoU of 0.925, whereas UNet and Res-UNet have IoUs of 0.30 and 0.35, respectively. The results indicate that Mask RCNN is both more time effective and precise and can be used in forest fire detection systems.
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