Hyperspectral images (HSIs) contain rich spectral information and spatial information. How to apply these two information types and fully combine the correlation between them remains a challenge worthy of further research and discussion. In this study, a multi-branch-multi-scale residual fusion network (MB-MS-RFN) for HSI classification is proposed. First, a 3D multi-branch-multi-scale convolution residual network, which can acquire image features of different scale in the training process and consider the correlation between spectral information and spatial information, is developed. Instead of deepening the network, the multi-branch structure widens the network horizontally to obtain more accurate classification. Finally, the different levels of HSI features are fused to obtain better classification results. Several experiments have been carried out to verify the proposed framework, and the results have demonstrated that the proposed MB-MS-RFN framework can improve the classification performance of HSIs. The performance of the MB-MS-RFN was evaluated using the Indian Pines, Pavia University, and Kennedy Space Center datasets; the performances’ overall accuracies were 99.66%, 99.92%, and 99.97%, respectively. The results from a series of experiments confirm that the proposed method offers several advantages in classification accuracy compared with five other methods.

中文翻译：

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多分支多尺度残差融合网络的高光谱图像分类

高光谱图像（HSI）包含丰富的光谱信息和空间信息。如何应用这两种信息类型并充分结合它们之间的相关性仍然是一个值得进一步研究和讨论的挑战。在这项研究中，提出了一种用于HSI分类的多分支多尺度残差融合网络（MB-MS-RFN）。首先，开发了一种3D多分支多尺度卷积残差网络，该网络可以在训练过程中获取不同尺度的图像特征，并考虑光谱信息与空间信息之间的相关性。多分支结构没有加深网络，而是水平扩展了网络以获得更准确的分类。最后，融合不同级别的HSI功能以获得更好的分类结果。已经进行了一些实验来验证所提出的框架，结果表明所提出的MB-MS-RFN框架可以提高HSI的分类性能。MB-MS-RFN的性能是使用Indian Pines，Pavia University和Kennedy Space Center数据集进行评估的；表演的整体准确率分别为99.66％，99.92％和99.97％。一系列实验的结果证实，与其他五种方法相比，该方法在分类准确性方面具有多个优势。表演的整体准确率分别为99.66％，99.92％和99.97％。一系列实验的结果证实，与其他五种方法相比，该方法在分类准确性方面具有多个优势。表演的整体准确率分别为99.66％，99.92％和99.97％。一系列实验的结果证实，与其他五种方法相比，该方法在分类准确性方面具有多个优势。