A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2175 |
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| _version_ | 1849428693270659072 |
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| author | Rui Zou Zhihui Xin Guisheng Liao Penghui Huang Rui Wang Yuhu Qiao |
| author_facet | Rui Zou Zhihui Xin Guisheng Liao Penghui Huang Rui Wang Yuhu Qiao |
| author_sort | Rui Zou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c0b3f2ea7494d5eb697d9e791dec793 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4c0b3f2ea7494d5eb697d9e791dec7932025-08-20T03:28:37ZengMDPI AGRemote Sensing2072-42922025-06-011713217510.3390/rs17132175A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category ImbalanceRui Zou0Zhihui Xin1Guisheng Liao2Penghui Huang3Rui Wang4Yuhu Qiao5School of Physics and Electronic Information Technology, Yunnan Normal University, Kunming 650500, ChinaSchool of Physics and Electronic Information Technology, Yunnan Normal University, Kunming 650500, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Physics and Electronic Information Technology, Yunnan Normal University, Kunming 650500, ChinaSchool of Physics and Electronic Information Technology, Yunnan Normal University, Kunming 650500, ChinaFire 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.https://www.mdpi.com/2072-4292/17/13/2175FDE U-Netfeature fusionfire segmentationmultispectral remote sensing images |
| spellingShingle | Rui Zou Zhihui Xin Guisheng Liao Penghui Huang Rui Wang Yuhu Qiao A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance Remote Sensing FDE U-Net feature fusion fire segmentation multispectral remote sensing images |
| title | A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance |
| title_full | A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance |
| title_fullStr | A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance |
| title_full_unstemmed | A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance |
| title_short | A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance |
| title_sort | fire segmentation method with flame detail enhancement u net in multispectral remote sensing images under category imbalance |
| topic | FDE U-Net feature fusion fire segmentation multispectral remote sensing images |
| url | https://www.mdpi.com/2072-4292/17/13/2175 |
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