Institute of Remote Sensing and Earth Sciences, School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
SenseTime Research, Shenzhen 518000, China.
Sensors (Basel). 2023 Mar 16;23(6):3190. doi: 10.3390/s23063190.
Recently, convolution neural networks have been widely used in hyperspectral image classification and have achieved excellent performance. However, the fixed convolution kernel receptive field often leads to incomplete feature extraction, and the high redundancy of spectral information leads to difficulties in spectral feature extraction. To solve these problems, we propose a nonlocal attention mechanism of a 2D-3D hybrid CNN (2-3D-NL CNN), which includes an inception block and a nonlocal attention module. The inception block uses convolution kernels of different sizes to equip the network with multiscale receptive fields to extract the multiscale spatial features of ground objects. The nonlocal attention module enables the network to obtain a more comprehensive receptive field in the spatial and spectral dimensions while suppressing the information redundancy of the spectral dimension, making the extraction of spectral features easier. Experiments on two hyperspectral datasets, Pavia University and Salians, validate the effectiveness of the inception block and the nonlocal attention module. The results show that our model achieves an overall classification accuracy of 99.81% and 99.42% on the two datasets, respectively, which is higher than the accuracy of the existing model.
最近，卷积神经网络在高光谱图像分类中得到了广泛应用，取得了优异的性能。然而，固定卷积核感受野往往导致特征提取不完整，光谱信息的高冗余度导致光谱特征提取困难。为了解决这些问题，我们提出了一种二维-三维混合卷积神经网络（2-3D-NL CNN）的非局部注意机制，该机制包括 inception 块和非局部注意模块。inception 块使用不同大小的卷积核为网络配备多尺度感受野，以提取地物的多尺度空间特征。非局部注意模块使网络在空间和光谱维度上获得更全面的感受野，同时抑制光谱维度的信息冗余，使光谱特征的提取更加容易。在两个高光谱数据集，帕维亚大学和萨利恩斯的实验验证了 inception 块和非局部注意模块的有效性。结果表明，我们的模型在两个数据集上的总体分类准确率分别达到 99.81%和 99.42%，高于现有模型的准确率。
