A Comprehensive Feature Extraction Network for Deep-Learning-Based Wildfire Detection in Remote Sensing Imagery

As global climate change escalates, wildfires have emerged as a critical form of natural disaster, presenting substantial risks to ecosystems, public safety, and economic development. While satellite remote sensing has been extensively utilized for wildfire monitoring, current methodologies face limitations in addressing complex backgrounds and environmental variations. These techniques usually depend on set thresholds or the extraction of local features, which can lead to incorrect positives and overlooked detections. Consequently, existing methods inadequately capture the comprehensive characteristics of fire points. To mitigate these challenges, this study proposes a deep-learning-based fire point detection method that integrates Swin Transformer and BiLSTM for the extraction of the multi-dimensional features associated with fire points. This research represents the inaugural application of the Swin Transformer in the context of fire point detection, leveraging its self-attention mechanism to discern global dependencies and fire point information within complex environments. By amalgamating features at various levels, the proposed method significantly improves the accuracy and robustness of fire point detection. Experimental findings demonstrate that this method surpasses traditional models such as DenseNet, SimpleCNN, and Multi-Layer Perceptron (MLP) across multiple performance metrics, including accuracy, recall, and F1 score.