Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (3): 138-151.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.03.010

doi: 10.11871/jfdc.issn.2096-742X.2023.03.010

• Technology and Application • Previous Articles     Next Articles

Hyperspectral Image Classification Method Combining Transformer and Multi-Layer Feature Aggregation

CHEN Dong1(),LI Ming1,*(),CHEN Shuwen2   

  1. 1. College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
    2. College of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
  • Received:2022-10-10 Online:2023-06-20 Published:2023-06-21

Abstract:

[Background] In recent years, the convolutional neural network has been widely used in hyper-spectral image classification tasks and achieved excellent classification performance. However, the convolutional neural network still has many limitations. For example, the convolution has a small receiving area and the downsampling operation will cause the loss of spatial information. [Objective] To solve the above problems, this paper proposes a hyperspectral image classification method that combines transformer and multi-layer feature aggregation (TMFANet). [Methods] First, TMFANet uses 3D CNN and 2D CNN layers to extract the shallow spatial-spectral features of the image. Then, a method based on the dense convolution Transformer module is proposed to extract the global features of images. Then, a multi-layer feature aggregation module is proposed to extract image features at different levels. Finally, the extracted abstract features are sent to a classifier for classification. To verify the effectiveness of TMFANet, a series of experiments are carried out on three public data sets, Indian pines, Pavia University and Salinas. [Results] The experimental results show that the classification performance of TMFANet proposed in this paper is better than other current methods.

Key words: Convolution neural network, Image processing, Hyperspectral image classification, Transformer, Characteristic aggr-egation