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Spectral-spatial attention bilateral network for hyperspectral image classification

  • role： First author第一作者
  • Affiliation：

    School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401

  • Email：yx374448357@163.com
  • Introduction：杨星，研究方向为深度学习在高光谱图像上的应用。E-mail：yx374448357@163.com
YANG Xing,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401

  • Email：chiyueliuxin@126.com
  • Introduction：池越，研究方向为图像处理。 E-mail：chiyueliuxin@126.com
CHI Yue*,  
  • Affiliation：

    School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401

ZHOU Yatong,  
  • Affiliation：

    School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401

WANG Yang

Resümee

HypeSspectral Image Classification (HSIC) is a pixel-level classification problem, and it involves classifying each pixel in the hyperspectral image and confirming the pixel category. However, discriminative features in HSIC task are difficult to acquire and learn, and the extraction of sufficient and effective features directly affects the classification results. In the past few years, Convolutional Neural Networks (CNNs) have achieved better results in HSIC, but the high dimensionality of hyperspectral images and the equal processing of all bands by CNNs have limited the performance of CNN. This study proposes an end-to-end Spectral-Spatial Attention Bilateral Network (SSABN) for HSIC. The network directly uses 3D blocks of the original image as input data without the complicated preprocessing. First, the original data are processed through the spectral-spatial attention module to enhance the useful bands or pixels for classification and suppress invalid information. Then, the spatial and context paths of the bilateral network are designed. The spatial path has three layers, and each layer is composed of convolution, batch normalization, and Relu activation function to extract spatial information. The context path is composed of three downsampling and attention refining modules. The downsampling is used to provide receptive field, and the attention refinement module is used to refine downsampling features. Finally, a feature fusion module is designed to fuse different levels of features through maximum pooling and average pooling for generating discriminative features. Compared with common CNN, SSABN can adaptively enhance effective information, extract more abstract discriminative features, and consume less training time. Experimental results show that SSABN has good fitting ability in different training sample ratios. In the results of ablation experiments, the accuracy of the spectral-spatial attention mechanism is 1%—2% higher than those of other mainstream attention mechanisms, and the feature fusion module can improve the discrimination of extracted features. In the experiments of three public datasets, the classification accuracy of SSABN is higher than 99%, and the training time is less than those of other methods. The classification performance of SSABN is better than those of other hyperspectral image classification algorithms, while reducing its training time can more effectively improve accuracy and efficiency.

Schlüsselwort

remote sensing;convolutional neural network;deep learning;Feature fusion;Indian Pine dataset;Pavia University dataset;Salinas dataset

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