Gao Hongmin, Zhang Yiyan, Zhang Yunfei, Chen Zhonghao, Li Chenming, Zhou Hui
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, No. 8 Focheng Road, Nanjing 211100, China.
College of Computer and Information, Hohai University, No. 8 Focheng Road, Nanjing 211100, China.
Micromachines (Basel). 2021 Oct 18;12(10):1271. doi: 10.3390/mi12101271.
In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.
近年来，高光谱图像分类（HSI）引起了广泛关注。基于卷积神经网络的各种方法取得了出色的分类结果。然而，它们大多存在光谱-空间特征利用不足、信息冗余和收敛困难等缺陷。为了解决这些问题，提出了一种用于高光谱图像分类的新型3D-2D多分支特征融合与密集注意力网络。具体来说，3D多分支特征融合模块在空间和光谱维度上整合多个感受野以获得浅层特征。然后，一个2D密集连接注意力模块由密集连接层和空间通道注意力块组成。前者用于缓解梯度消失并在训练过程中增强特征重用。后者强调有意义的特征并沿两个主要维度：通道和空间轴抑制干扰信息。在四个基准高光谱图像数据集上的实验结果表明，该模型能够有效提高分类性能，具有很强的鲁棒性。
