Hyperspectral Image Classification Method Based on 2D-3D CNN and Multibranch Feature Fusion
Hyperspectral Image Classification Method Based on 2D-3D CNN and Multibranch Feature Fusion
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DOI：
10.1109/jstars.2020.3024841
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发表时间：
2020-01-01
影响因子：
5.5
通讯作者：
Fu, Peng
中科院分区：
文献类型：
--
作者：
Ge, Zixian;Cao, Guo;Fu, Peng
卷积神经网络（CNN）的出现极大地促进了高光谱图像（HSI）分类技术的发展。然而，收购恒指的难度很大。训练样本的缺乏是导致分类性能低下的主要原因。传统的基于CNN的方法主要使用二维CNN进行特征提取，这使得HSI的带间相关性没有得到充分利用。3-D CNN提取联合频谱-空间信息表示，但它依赖于更复杂的模型。太深或太浅的网络都不能很好地提取图像特征。为了解决这些问题，我们提出了一种基于2D-3D CNN和多分支特征融合的HSI分类方法。我们首先结合联合收割机2-D CNN和3-D CNN来提取图像特征。然后，利用多分支神经网络，在光谱维上提取并融合了由浅入深的三种特征。最后，融合后的特征被传递到几个全连接层和一个softmax层，以获得分类结果。此外，我们的网络模型利用最先进的激活函数Mish来进一步提高分类性能。我们的实验结果，进行了广泛使用的HSI数据集，表明该方法取得了更好的性能比现有的替代品。
The emergence of a convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. The lack of training samples is the primary cause of low classification performance. The traditional CNN-based methods mainly use the 2-D CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3-D CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. Also, too deep or too shallow network cannot extract the image features well. To tackle these issues, we propose an HSI classification method based on the 2D-3D CNN and multibranch feature fusion. We first combine 2-D CNN and 3-D CNN to extract image features. Then, by means of the multibranch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension. Finally, the fused features are passed into several fully connected layers and a softmax layer to obtain the classification results. In addition, our network model utilizes the state-of-the-art activation function Mish to further improve the classification performance. Our experimental results, conducted on four widely used HSI datasets, indicate that the proposed method achieves better performance than the existing alternatives.