Spectral and spatial details abound in hyperspectral data. However, there are few labeled samples available, and obtaining them is expensive and time-consuming. As a result, improving the classification accuracy of HSI with limited training samples is a critical challenge. We propose a double-branch network with attention feature fusion (DBN-AFF) for HSI classification. One branch extracts spectral features, and the other extracts spatial features. It is capable of effectively preventing cross-branch interference. Furthermore, the AFF module is used instead of the channel concatenation method. It effectively fuses spectral and spatial features by considering the relationship between different channels. On the Indian Pines, Pavia University, and Salinas Valley datasets, DBN-AFF achieves 97.44%, 98.37%, and 97.60% on limited training samples, respectively.

中文翻译：

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基于注意力特征融合的双分支网络的高光谱图像分类

光谱和空间细节在高光谱数据中比比皆是。然而，可用的标记样本很少，获取它们既昂贵又耗时。因此，在训练样本有限的情况下提高 HSI 的分类精度是一项关键挑战。我们提出了一种用于 HSI 分类的具有注意力特征融合（DBN-AFF）的双分支网络。一个分支提取光谱特征，另一个分支提取空间特征。它能够有效地防止跨分支干扰。此外，使用 AFF 模块代替通道级联方法。它通过考虑不同通道之间的关系，有效地融合了光谱和空间特征。在印度松树、帕维亚大学和萨利纳斯谷数据集上，DBN-AFF 在有限的训练样本上达到了 97.44%、98.37% 和 97.60%，