Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 151-156.doi: 10.11896/jsjkx.190500147

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Local Gabor Convolutional Neural Network for Hyperspectral Image Classification

WANG Yan, WANG Li   

  1. College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China
  • Received:2019-05-27 Online:2020-06-15 Published:2020-06-10
  • About author:WANG Yan,born in 1971,professor,is a member of China Computer Federation.Her main research interests include pattern recognition and artificial intelligence.
  • Supported by:
    This work was supported by the Key R&D Project of Gansu Province (18YF1GA060).

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Abstract: In order to solve the problem of insufficient utilization of hyperspectral image features,a new classification method based on spatial-spectral features was proposed.Firstly,principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce the dimension of hyperspectral images.Secondly,the Gabor kernel is introduced to design a Local Gabor Convolution (LGC) layer based on the local Gabor kernel.Finally,a new convolutional neural network (LGCNN) is designed based on the LGC layer for classification.The proposed method is validated on Indian Pines and Salinas scene datasets and compared with other classical classification methods.Experiment results show that this method not only saves the learning parameters greatly,reduces the complexity of the model,but also shows good classification performance.Its overall accuracy can reach 99%,the average classification accuracy can reach more than 98%,and the Kappa coefficient can reach more than 98%.

Key words: Convolutional neural network, Deep learning, Gabor filtering, Hyperspectral image classification, Spatial-spectral information

CLC Number: 

  • TP751.1
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