Recently, deep learning (DL)-based hyperspectral image classification (HSIC) has attracted substantial attention. Many works based on the convolutional neural network (CNN) model have been certificated to be significantly successful for boosting the performance of HSIC. However, most of these methods extract features by using a fixed convolutional kernel and ignore multiscale features of the ground objects of hyperspectral images (HSIs). Although some recent methods have proposed multiscale feature extraction schemes, more computing and storage resources were consumed. Moreover, when using CNN to implement HSI classification, many methods only use the high-level semantic information extracted from the end of the network, ignoring the edge information extracted from shallow layers of the network. To settle the preceding two issues, a novel HSIC method based on hierarchical shrinkage multiscale network and the hierarchical feature fusion is proposed, with which the newly proposed classification framework can fuse features generated by both of multiscale receptive field and multiple levels. Specifically, multidepth and multiscale residual block (MDMSRB) is constructed by superposition dilated convolution to realize multiscale feature extraction. Furthermore, according to the change of feature size in different stages of the neural networks, we design a hierarchical shrinkage multiscale feature extraction network by pruning MDMSRB to reduce the redundancy of network structure. In addition, to make full use of the features extracted in each stage of the network, the proposed network hierarchically integrates low-level edge features and high-level semantic features effectively. Experimental results demonstrate that the proposed method achieves more competitive performance with a limited computational cost than other state-of-the-art methods.

中文翻译：

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用于具有分层特征融合的高光谱图像分类的分层收缩多尺度网络


最近，基于深度学习（DL）的高光谱图像分类（HSIC）引起了广泛关注。许多基于卷积神经网络（CNN）模型的工作已被证明在提高 HSIC 性能方面取得了显着成功。然而，这些方法大多数使用固定的卷积核来提取特征，而忽略了高光谱图像（HSIs）地物的多尺度特征。尽管最近的一些方法提出了多尺度特征提取方案，但消耗了更多的计算和存储资源。而且，在使用CNN实现HSI分类时，许多方法仅使用从网络末端提取的高层语义信息，而忽略了从网络浅层提取的边缘信息。为了解决上述两个问题，提出了一种基于分层收缩多尺度网络和分层特征融合的HSIC方法，新提出的分类框架可以融合多尺度感受野和多个层次生成的特征。具体来说，通过叠加扩张卷积构造多深度多尺度残差块（MDMSRB）来实现多尺度特征提取。此外，根据神经网络不同阶段特征尺寸的变化，通过剪枝MDMSRB来设计分层收缩多尺度特征提取网络，以减少网络结构的冗余。此外，为了充分利用网络各个阶段提取的特征，所提出的网络有效地分层集成低层边缘特征和高层语义特征。 实验结果表明，与其他最先进的方法相比，所提出的方法以有限的计算成本实现了更具竞争力的性能。