Accurate classification of forest fires in aerial images using ensemble model

This paper proposes a method to identify forest fires in aerial images using three different convolutional neural networks (CNNs). Unlike general approaches that make use of a single CNN to classify the images, the proposed solution uses the outcomes of different CNNs and considers the most predicted class. This method overcomes the problems associated with using a single CNN, such as low accuracy due to the drawbacks associated with that model. The three different classifiers used here are InceptionV3, VGG-16, and ResNet50. Classification is carried out based on the presence of fire or smoke features in the images. The individual predictions are combined using max-ensembling. The performance is analyzed using metrics like precision, recall, accuracy and F1-score. From the work, it was found that the combined model resulted in an accuracy of 95.8%. The results confirm that the final model provides greater classification accuracy than the individual models. The proposed method can be used to predict forest fires from live aerial images more accurately and help reduce the damage caused.