Hybrid unsupervised methods and inject-multiply morphological features for mapping wildfire burned areas with multi-spectral satellite data

Mapping wildfire burned areas using satellite imagery is essential for immediate response measures as well as for long-term recovery planning. These maps provide critical information to response teams, allowing them to effectively allocate resources and prioritize affected areas. This study focuses on the aim of providing accurate maps of areas affected by wildfires in the Guzli region near Bukhara province in Uzbekistan. The benchmark dataset from the study area, named UZB-WF2022, which indicates the country name and year of occurrence, and includes Sentinel-2 and Plant-Scope multispectral multi-resolution images. This study uses a mixture of unsupervised deep learning with the k-means algorithm to accurately identify and map burned areas. The core of the proposed method is an autoencoder model designed with 3-dimensional convolutional layers. This autoencoder is mixed with the k-means algorithm in the latent space of the model and uses the k-means cost function to improve the training process. In addition, the proposed method has an attention mechanism based on morphological operations called Inject-Multiply. This mechanism integrates morphological features obtained from post-wildfire vegetation index data and moisture changes captured in Sentinel-2 images, focusing on enriching features related to the shape and boundaries of burned areas. This study evaluates the effectiveness of the proposed method in labeling, identifying, and mapping burned areas using benchmark datasets, using different evaluation criteria. The model achieves an accuracy of over 93% on the UZB-WF2022 dataset. This approach increases the accuracy of burned area detection in similar datasets and facilitates more informed decision-making for post-wildfire recovery and land management.