Patent ID: 11941865
Assignee: WUHAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 5:
6. The hyperspectral image classification method based on context-rich networks according to claim 1, wherein:
in the context-rich information capturing module, before performing intra-feature context information extraction, a dimension of the feature X={Xi∈C0×H×W}i=1Ns obtained by the feature extraction module is reduced with a 1×1 convolution, wherein C0, H and W are a channel number, a width and a height of X, respectively, and Ns is a number of paths; in each path, the intra-feature relationship extraction is respectively performed in each grid to complete the spatial context information aggregation; for a path i that has n×n grids, that is, divided into n parts in a width direction and a height direction, respectively, wherein size of each grid is, ⌊
    
     H
     n
    
    ⌋
   
   ×
   
    ⌊
    
     W
     n
    
    ⌋
   
  
  ,, firstly, three 1×1 convolutions are respectively used to map the dimension-reduced X to three features: Qi, Ki, Vi, for a grid g, the features within a coverage of this grid are taken respectively from features Qi, Ki, Vi and perform matrix shapes transformation, wherein both a channel number of Qi, Ki are c1, and the channel number of Vi is c2, obtaining qg∈c1×hw, kg∈c1×hw, vg∈c2×hw; using the qg, kg, vg respectively as a query feature, a key feature and a value feature for realizing the spatial context information extraction process in the grid, and generating a new feature pg∈c2×h×w through the matrix shape transformation, wherein, h
   =
   
    ⌊
    
     H
     n
    
    ⌋
   
  
  ,
  
   w
   =
   
    ⌊
    
     W
     n
    
    ⌋
   
  
  ,, this process can be formulated as

pg=softmax(qgTkg)vgT 

then, pg is filled to a position covered by grid g in the dimension-reduced feature Xi, while all the other grids are processed in same way, thus obtaining a new feature Pi that completes spatial contexts in that path, and each path will generate a new feature, denoted as a feature set P={Pi∈C1×H×W}i=1Ns, wherein C1 is a number of channels, C1=c2=4c1.