Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification. However, high-dimensional HSI dataset versus limited training samples is easy to cause the overfitting phenomenon in deep neural networks. Additionally, the intraclass distance of the embedding features extracted through the softmax-based CNNs may be greater than that of the interclass, which makes it difficult to further improve the classification accuracy. To address these issues, this article proposes a deep prototypical network with hybrid residual attention, which can effectively investigate the spectral–spatial information in the HSI. Specifically, in order to improve the generalization capability of the model, feature extraction with a hybrid residual attention module is presented to enhance the critical spectral–spatial features and suppress the useless ones in the classification task. Furthermore, a novel discriminant distance-based cross-entropy loss is proposed to increase the intraclass compactness, to obtain more superior results. Extensive experiments on three benchmark datasets are carried out to convincingly evaluate the proposed framework. With the generation of optimal prototypes representing each class and more discriminative embedding features, encouraging classification results are achieved compared with state-of-the-art methods.

中文翻译：

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用于高光谱图像分类的具有混合剩余注意的深度原型网络

最近，卷积神经网络 (CNN) 在模式识别方面引起了极大的关注，并在高光谱图像 (HSI) 分类中表现出优异的性能。然而，高维 HSI 数据集与有限的训练样本相比，容易导致深度神经网络中的过拟合现象。此外，通过基于 softmax 的 CNN 提取的嵌入特征的类内距离可能大于类间的距离，这使得进一步提高分类精度变得困难。为了解决这些问题，本文提出了一种具有混合残差注意力的深度原型网络，可以有效地研究 HSI 中的谱空间信息。具体来说，为了提高模型的泛化能力，提出了带有混合残差注意模块的特征提取，以增强关键的光谱空间特征并抑制分类任务中无用的特征。此外，提出了一种新的基于判别距离的交叉熵损失来增加类内的紧凑性，以获得更好的结果。对三个基准数据集进行了大量实验，以令人信服地评估所提出的框架。通过生成代表每个类的最佳原型和更具辨别力的嵌入特征，与最先进的方法相比，实现了令人鼓舞的分类结果。提出了一种新的基于判别距离的交叉熵损失来增加类内紧凑性，以获得更优越的结果。对三个基准数据集进行了大量实验，以令人信服地评估所提出的框架。通过生成代表每个类的最佳原型和更具辨别力的嵌入特征，与最先进的方法相比，实现了令人鼓舞的分类结果。提出了一种新的基于判别距离的交叉熵损失来增加类内紧凑性，以获得更优越的结果。对三个基准数据集进行了大量实验，以令人信服地评估所提出的框架。通过生成代表每个类的最佳原型和更具辨别力的嵌入特征，与最先进的方法相比，实现了令人鼓舞的分类结果。