A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance

作者：Rui Zou, Zhihui Xin, Guisheng Liao, Penghui Huang, Rui Wang, Yang Qiao · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17132175 · 被引用次数：4 · 研究领域：Fire Detection and Safety Systems、Video Surveillance and Tracking Methods、Fire effects on ecosystems

Fire poses a serious threat to the global economy, environment, and social stability, highlighting the need for rapid and accurate fire detection. Remote sensing combined with deep learning has outperformed traditional fire assessment methods. However, in early fire stages, small flame areas, class imbalance, and weak feature extraction hinder detection accuracy. This study proposes an end-to-end segmentation model called Flame Detail Enhancement U-Net (FDE U-Net), using Landsat-8 multispectral remote sensing data. The model incorporates the self-Attention and Convolutional mixture (ACmix) module and the Convolutional Block Attention Module (CBAM) into the encoder of the Residual U-Net. ACmix integrates self-attention and convolution to capture global semantic features while maintaining computational efficiency, improving both contextual awareness and local detail. CBAM enhances flame recognition by weighting important channel features and focusing spatially on small flame areas, helping address the class imbalance problem. Additionally, Haar wavelet downsampling is applied to retain image detail and improve the detection of small-scale flame regions. Experimental results show that the FDE U-Net model exhibits robust performance in fire detection, accurately extracting flame regions even when their proportion is low and the background is complex. The F1 score reaches 95.97%, significantly improving the class imbalance problem.