The representation power of convolutional neural network (CNN) models for hyperspectral image (HSI) analysis is in practice limited by the available amount of the labeled samples, which is often insufficient to sustain deep networks with many parameters. We propose a novel approach to boost the network representation power with a two-stream 2-D CNN architecture. The proposed method extracts simultaneously, the spectral features and local spatial and global spatial features, with two 2-D CNN networks and makes use of channel correlations to identify the most informative features. Moreover, we propose a layer-specific regularization and a smooth normalization fusion scheme to adaptively learn the fusion weights for the spectral–spatial features from the two parallel streams. An important asset of our model is the simultaneous training of the feature extraction, fusion, and classification processes with the same cost function. Experimental results on several hyperspectral data sets demonstrate the efficacy of the proposed method compared with the state-of-the-art methods in the field.

中文翻译：

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用于高光谱图像分类的双流卷积神经网络的深度特征融合

用于高光谱图像 (HSI) 分析的卷积神经网络 (CNN) 模型的表示能力实际上受到标记样本的可用数量的限制，这通常不足以维持具有许多参数的深度网络。我们提出了一种新颖的方法，通过两流 2-D CNN 架构来提高网络表示能力。所提出的方法使用两个二维 CNN 网络同时提取光谱特征以及局部空间和全局空间特征，并利用通道相关性来识别信息量最大的特征。此外，我们提出了特定于层的正则化和平滑归一化融合方案，以从两个并行流中自适应地学习光谱空间特征的融合权重。我们模型的一个重要资产是使用相同的成本函数同时训练特征提取、融合和分类过程。与该领域最先进的方法相比，在几个高光谱数据集上的实验结果证明了所提出的方法的有效性。