基于迁移学习和通道注意力的遥感图像场景分类
Remote sensing image scene classification based on transfer learning and channel attention
投稿时间:2022-11-10  修订日期:2023-02-16
DOI:
中文关键词:  遥感图像  场景分类  卷积神经网络  迁移学习  通道注意力
英文关键词:remote sensing image  scene classification  convolutional neural network  transfer learning  channel attention
基金项目:
作者单位邮编
舒薪行 天津理工大学 300384
温显斌* 天津理工大学 300384
袁立明 天津理工大学 
徐海霞 天津理工大学 
史芙蓉 天津理工大学 
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中文摘要:
      针对遥感图像场景分类任务中训练样本数量少及遥感图像背景复杂等问题,本文将迁移学习和通道注意力引入到卷积神经网络中,提出基于迁移学习和通道注意力的遥感图像场景分类方法。该方法首先选用经过ImageNet自然数据集预训练的2个卷积神经网络作为主干,同时引入通道注意力机制,自适应地增强主要特征,抑制次要特征;然后融合这两个网络提取的特征进行分类;最后采用微调迁移学习的方式实现目标域上学习与分类。提出的方法在几个经典的公共数据集上进行了评估,实验结果证明了本文提出的方法在遥感图像场景分类中达到与其它先进方法相当的性能。
英文摘要:
      In order to solve the problems of small number of training samples and complex background of remote sensing images, this paper introduces transfer learning and channel attention into convolutional neural network, and proposes a remote sensing image scene classification method based on transfer learning and channel attention. Firstly, this method selects two convolutional neural networks pre-trained by ImageNet natural dataset as the backbone, and introduces the channel attention mechanism to adaptively enhance the main features and suppress the secondary features. Then the features extracted from these two networks are fused for classification. Finally, fine-tuning transfer learning is used to realize learning and classification in the target domain. The proposed method is evaluated on several classical public datasets, and the experimental results show that the proposed method achieves the same performance as other advanced methods in remote sensing image scene classification.
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