In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands for classification. Moreover, for patchwise-based CNN models, equal treatment of spatial information from the pixel-centered neighborhood also hinders the performance of these methods. In this article, we propose an end-to-end residual spectral–spatial attention network (RSSAN) for HSI classification. The RSSAN takes raw 3-D cubes as input data without additional feature engineering. First, a spectral attention module is designed for spectral band selection from raw input data by emphasizing useful bands for classification and suppressing useless bands. Then, a spatial attention module is designed for the adaptive selection of spatial information by emphasizing pixels from the same class as the center pixel or those are useful for classification in the pixel-centered neighborhood and suppressing those from a different class or useless. Second, two attention modules are also used in the following CNN for adaptive feature refinement in spectral–spatial feature learning. Third, a sequential spectral–spatial attention module is embedded into a residual block to avoid overfitting and accelerate the training of the proposed model. Experimental studies demonstrate that the RSSAN achieved superior classification accuracy compared with the state of the art on three HSI data sets: Indian Pines (IN), University of Pavia (UP), and Kennedy Space Center (KSC).

中文翻译：

---

残留光谱-空间注意网络用于高光谱图像分类

在过去的五年中，已经引入了深度学习来解决高光谱图像（HSI）分类并表现出良好的性能。特别地，基于卷积神经网络（CNN）的HSI分类方法取得了长足的进步。但是，由于HSI的高维性和所有频带的均等处理，这些方法的性能受到了从无用频带中进行分类的学习特征的阻碍。此外，对于基于补丁的CNN模型，来自以像素为中心的邻域的空间信息的同等对待也阻碍了这些方法的性能。在本文中，我们提出了一种用于HSI分类的端到端残留光谱空间关注网络（RSSAN）。RSSAN将原始3-D多维数据集用作输入数据，而无需进行其他功能设计。第一，频谱关注模块通过强调用于分类的有用频段和抑制无用频段，设计用于从原始输入数据中选择频段。然后，通过强调来自与中心像素相同类别的像素或在以像素为中心的邻域中进行分类的像素，并抑制来自不同类别或无用的像素，将空间关注模块设计为自适应选择空间信息。其次，在接下来的CNN中，两个注意模块也用于频谱空间特征学习中的自适应特征细化。第三，将顺序光谱空间注意模块嵌入到残差块中，以避免过度拟合并加快对所提出模型的训练。