Semi-supervised segmentation of forest fires from UAV remote sensing images via panoramic feature fusion and pixel contrastive learning

Introduction Wildfire detection and segmentation play a critical role in environmental monitoring and disaster prevention. However, existing deep learning-based segmentation models often struggle to identify wildfire boundaries accurately due to complex image features and limited annotated data. Methods We propose a novel segmentation network called PPCNet, which integrates three key modules: a Panoramic Feature Fusion (PFF) module for multi-scale feature extraction, a Dense Feature Fusion Encoder (DFFE) to capture contextual details, and a Local Detail Compensation (LDC) loss function to enhance boundary accuracy. Additionally, we design a pseudo-label optimization framework to leverage unlabeled data effectively. Results Experiments were conducted on multiple wildfire datasets, and the results show that PPCNet achieves superior performance compared to state-of-the-art methods. Our model demonstrates significant improvements in segmentation accuracy and boundary localization, validated through quantitative metrics and visual comparisons. Discussion The integration of PFF, DFFE, and LDC components enables PPCNet to generalize well across different wildfire scenarios. The use of pseudo-labeling further enhances performance without requiring additional labeled data, making it suitable for real-world deployment in wildfire monitoring systems.