Fusion of Deep Features of Wavelet Transform for Wildfire Detection

Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. Wildfire is the leading cause of deforestation, where massive forest areas are annually lost due to the failure to identify and predict forest fires. Accordingly, early detection of wildfires is crucial to inform operational and firefighting teams to prevent fires from advancing. This study analyzes images taken by unmanned aerial vehicles for wildfire detection. For this purpose, the two-dimensional discrete wavelet transform was first performed on the images. Next, due to its superior ability, a convolutional neural network was utilized to extract deep features from wavelet transform sub-bands. Then, the features obtained from each sub-band were merged to create the final feature vector. Afterward, multidimensional scaling was employed to reduce the extracted non-useful features. Ultimately, the presence or absence of wildfire locations in the images was detected using proper classifiers. The proposed method reaches an accuracy and F1 score of 0.9684 and 0.9672, respectively, from the images of the FLAME dataset, indicating its efficiency in detecting the presence of wildfire locations. Thus, this method can significantly contribute to the on-time and prompt firefighting operations and prevent extensive damage to forests.