| 基于空谱注意力机制及预激活残差网络的高光谱图像分类算法 |
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| 引用本文： | 袁芊芊,谢维信. 基于空谱注意力机制及预激活残差网络的高光谱图像分类算法[J]. 信号处理, 2022, 38(12): 2594-2605. DOI: 10.16798/j.issn.1003-0530.2022.12.014 |
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| 作者姓名： | 袁芊芊 谢维信 |
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| 作者单位： | 1.深圳大学ATR国防科技重点实验室, 广东 深圳 518060 |
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| 基金项目： | 深圳市高等院校稳定支持计划面上项目20200826154022001 |
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| 摘 要： | 面向高光谱图像分类的许多深度学习算法中，由于提取的空谱特征表示鉴别性不足，其模型的分类性能有待提高。针对该问题，本文提出了一种基于空谱注意力机制及预激活残差网络的高光谱图像分类算法。首先，设计了基于空谱注意力机制的空谱特征提取模块，对空谱特征进行重校准，为空谱特征在后续联合学习时能专注于更具辨别力的通道和空间位置提供保证；其次，设计了基于预激活残差网络的空谱特征联合学习模块，其中预激活残差网络改进了原始残差构建块的网络结构，从而能在利用注意力机制重校准的空谱特征的联合学习时捕获更具鉴别性的深层空谱特征，以提高分类器的分类性能。实验结果表明，和已有的一些高光谱图像分类算法相比，所提出的算法的分类准确率更高，表明该算法能有效地获得判别能力更强的空谱特征表示。
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| 关 键 词： | ??空谱注意力 预激活 残差网络 高光谱图像分类 |
| 收稿时间： | 2022-05-06 |
Hyperspectral Image Classification Based on Spatial Spectral Attention and Pre-Activation Residual Networks |
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| Affiliation: | 1.ATR Key Laboratory of Shenzhen University, Shenzhen, Guangdong 518060, China2.Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, Guangdong 518060, China |
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| Abstract: | ? ?In many deep learning algorithms for hyperspectral image classification， the classification performance of their models needed to be improved due to the poor discriminant representation of extracted spatial spectral features. To solve this problem， this paper proposed a hyperspectral image classification algorithm based on spatial spectral attention and pre-activation residual network. Firstly， a spatial spectral feature extraction module based on the spatial spectral attention mechanism was designed to recalibrate the spatial spectral features， so as to ensure that the spatial spectral features can focus on more discriminative channels and spatial positions during subsequent joint learning； secondly， a joint learning module of spatial spectral features based on the pre-activation residual network was designed， in which the pre-activated residual network improved the network structure of the original residual building block， so that it could capture more discriminative deep spatial spectral features during joint learning of spatial spectral features recalibrated by attention mechanism， and therefore the classification performance of the classifier could be improved. The experimental results showed that compared with some existing hyperspectral image classification algorithms， the proposed algorithm could attain higher classification accuracy. It indicated that the algorithm can effectively obtain spatial spectral features representation with stronger discriminative ability. |
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