Convolutional neural networks (CNNs) have improved the accuracy of hyperspectral image (HSI) classification significantly. However, CNN models usually generate a large number of feature maps, which lead to high redundancy and cannot guarantee to effectively extract discriminative features for well characterizing the complex structures of HSIs. In this article, two novel mixed link networks (MLNets) are proposed to enhance the representational ability of CNNs for HSI classification. Specifically, the proposed mixed link architectures integrate the feature reusage property of the residual network and the capability of effective new feature exploration of the densely convolutional network, extracting more discriminative features from HSIs. Compared with the dual path architecture, the proposed mixed link architectures can further improve the information flow throughout the network. Experimental results on three hyperspectral benchmark datasets demonstrate that our MLNets achieve competitive results compared with other state-of-the-art HSI classification approaches.

中文翻译：

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混合链接网络的高光谱图像分类

卷积神经网络（CNN）大大提高了高光谱图像（HSI）分类的准确性。但是，CNN模型通常会生成大量的特征图，从而导致高度冗余，并且不能保证有效地提取区分特征以很好地表征HSI的复杂结构。在本文中，提出了两个新颖的混合链接网络（MLNet），以增强CNN在HSI分类中的表示能力。具体而言，所提出的混合链路体系结构融合了残差网络的特征重用特性和密集卷积网络的有效新特征探索能力，从而从HSI中提取了更多的判别特征。与双路径架构相比，提出的混合链路体系结构可以进一步改善整个网络的信息流。在三个高光谱基准数据集上的实验结果表明，与其他最新的HSI分类方法相比，我们的MLNets具有竞争优势。