融合注意力机制的SegFormer遥感影像道路识别
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国网福建省电力有限公司科技项目(52130423000X)


Road Recognition in Remote Sensing Images Using SegFormer Fused with Attention Mechanism
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    摘要:

    道路信息在遥感影像中具有重要意义和价值, 因此准确提取道路对于许多应用非常关键. 然而, 在进行道路识别时存在两个主要问题. 首先, 卫星影像的背景复杂多变, 而道路的形态也是复杂多样的, 这给道路的自动识别带来了挑战. 其次, 道路像素只占整个影像的很小一部分, 导致类别不平衡的问题. 为了解决上述问题, 本文提出了一种基于改进的SegFormer模型的卫星影像道路自动识别算法. 该算法采用了两个主要策略来改进识别效果. 第一, 在SegFormer编码器的各个阶段的输出端添加了空间注意力模块. 这个模块有助于减弱复杂背景的干扰, 同时增强对道路区域的关注. 通过引入空间注意力机制, 模型能够更好地捕捉到道路的特征, 从而提高识别准确性. 第二, 采用了一种混合损失函数, 结合了像素对比损失和交叉熵损失. 这样的损失函数能够更好地处理类别不平衡的问题, 使得模型更加关注道路类别的训练. 通过优化训练过程, 模型能够更好地学习到道路的特征表示, 从而提升识别准确率. 通过对比实验分析, 改进后的模型在测试集上的mIoU指标提升了约3.3%.

    Abstract:

    Road information is of great significance and value in remote sensing images, and thus the accurate extraction of roads is crucial for many applications. However, there are two main challenges in road recognition. Firstly, the background of satellite images is complex and diverse, while the morphology of roads is also complex and diverse, which poses a challenge to automatic road recognition. Secondly, road pixels only account for a small portion of the entire image, leading to class imbalance. To address these challenges, this study proposes an automatic road recognition algorithm based on an improved SegFormer model. The algorithm employs two main strategies to improve the recognition performance. Firstly, spatial attention modules are added to the output of each stage of the SegFormer encoder. This module helps to weaken the interference from complex backgrounds and enhance the attention to road areas. By introducing spatial attention mechanisms, the model can better capture the features of roads, thereby improving recognition accuracy. Secondly, a hybrid loss function that combines pixel contrast loss and cross-entropy loss is used. Such a loss function can better handle class imbalance problems and make the model place more focus on training road categories. By optimizing the training process, the model can better learn road feature representation, thereby improving recognition accuracy. Comparative experimental analysis shows that the improved model achieves an approximate 3.3% improvement in the mIoU metric on the test set.

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王晓杰,陈少康,闫皓炜,杨鹤猛,燕正亮,王森.融合注意力机制的SegFormer遥感影像道路识别.计算机系统应用,,():1-8

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  • 收稿日期:2024-04-02
  • 最后修改日期:2024-04-29
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  • 在线发布日期: 2024-09-24
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