Hyperspectral image classification based on multi-scale residual network with attention mechanism

04/26/2020 ∙ by Xiangdong Zhang, et al. ∙ 6

Compared with traditional machine learning methods, deep learning methods such as convolutional neural networks (CNNs) have achieved great success in the hyperspectral image (HSI) classification task. HSI contains abundant spatial and spectral information, but they also contain a lot of invalid information, which may introduce noises and weaken the performance of CNNs. In order to make full use of the useful information in HSI, we propose a multi-scale residual network integrated with the attention mechanism (MSRN-A) for HSI classification in this letter. In our method, we built two different multi-scale feature extraction blocks to extract the joint spatial-spectral features and the advanced spatial features, respectively. Moreover, a spatial-spectral attention module and a spatial attention module were set up to focus on the salient spatial parts and valid spectral information. Experimental results demonstrate that our method achieves high accuracy on the Indian Pines, Pavia University, and Salinas datasets. The source code can be found at https://github.com/XiangdongZ/MSRN-A.

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