Zhang Minghua, Duan Yuxia, Song Wei, Mei Haibin, He Qi
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
J Imaging. 2023 Jul 10;9(7):141. doi: 10.3390/jimaging9070141.
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification due to their limited receptive fields and deep architectures. To alleviate these limitations, we propose an effective HSI classification network based on multi-head self-attention and spectral-coordinate attention (MSSCA). Specifically, we first reduce the redundant spectral information of HSI by using a point-wise convolution network (PCN) to enhance discriminability and robustness of the network. Then, we capture long-range dependencies among HSI pixels by introducing a modified multi-head self-attention (M-MHSA) model, which applies a down-sampling operation to alleviate the computing burden caused by the dot-product operation of MHSA. Furthermore, to enhance the performance of the proposed method, we introduce a lightweight spectral-coordinate attention fusion module. This module combines spectral attention (SA) and coordinate attention (CA) to enable the network to better weight the importance of useful bands and more accurately localize target objects. Importantly, our method achieves these improvements without increasing the complexity or computational cost of the network. To demonstrate the effectiveness of our proposed method, experiments were conducted on three classic HSI datasets: Indian Pines (IP), Pavia University (PU), and Salinas. The results show that our proposed method is highly competitive in terms of both efficiency and accuracy when compared to existing methods.
在高光谱图像（HSI）分类中，卷积神经网络（CNN）已被广泛应用并取得了良好的性能。然而，基于CNN的方法由于其有限的感受野和深度架构，在实现准确且高效的HSI分类方面面临困难。为了缓解这些限制，我们提出了一种基于多头自注意力和光谱坐标注意力（MSSCA）的有效HSI分类网络。具体而言，我们首先使用逐点卷积网络（PCN）减少HSI的冗余光谱信息，以增强网络的可辨别性和鲁棒性。然后，我们通过引入改进的多头自注意力（M-MHSA）模型来捕捉HSI像素之间的长距离依赖性，该模型应用下采样操作来减轻由MHSA的点积操作引起的计算负担。此外，为了提高所提方法的性能，我们引入了一个轻量级的光谱坐标注意力融合模块。该模块结合了光谱注意力（SA）和坐标注意力（CA），使网络能够更好地权衡有用波段的重要性，并更准确地定位目标物体。重要的是，我们的方法在不增加网络复杂度或计算成本的情况下实现了这些改进。为了证明我们所提方法的有效性，我们在三个经典的HSI数据集上进行了实验：印第安纳松树（IP）、帕维亚大学（PU）和萨利纳斯。结果表明，与现有方法相比，我们所提方法在效率和准确性方面都具有很强的竞争力。
