期刊
FORESTS
卷 12, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/f12070937
关键词
forest image segmentation; PFE-UNet; PFE; spatial attention; channel-wise attention; DSC unit
类别
资金
- Fundamental Research Funds for the Central Universities [2572020BC07]
- National Natural Science Foundation of China [31570712]
This paper proposes a forest image segmentation method based on the PFE-UNet network, which achieves high accuracy real-time forest image segmentation through the introduction of pyramid feature extraction and attention modules.
The precise segmentation of forest areas is essential for monitoring tasks related to forest exploration, extraction, and statistics. However, the effective and accurate segmentation of forest images will be affected by factors such as blurring and discontinuity of forest boundaries. Therefore, a Pyramid Feature Extraction-UNet network (PFE-UNet) based on traditional UNet is proposed to be applied to end-to-end forest image segmentation. Among them, the Pyramid Feature Extraction module (PFE) is introduced in the network transition layer, which obtains multi-scale forest image information through different receptive fields. The spatial attention module (SA) and the channel-wise attention module (CA) are applied to low-level feature maps and PFE feature maps, respectively, to highlight specific segmentation task features while fusing context information and suppressing irrelevant regions. The standard convolution block is replaced by a novel depthwise separable convolutional unit (DSC Unit), which not only reduces the computational cost but also prevents overfitting. This paper presents an extensive evaluation with the DeepGlobe dataset and a comparative analysis with several state-of-the-art networks. The experimental results show that the PFE-UNet network obtains an accuracy of 94.23% in handling the real-time forest image segmentation, which is significantly higher than other advanced networks. This means that the proposed PFE-UNet also provides a valuable reference for the precise segmentation of forest images.
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