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

Claim 6:
7. The hyperspectral image classification method based on context-rich networks according to claim 6, wherein: a specific implementation process of extracting the inter-feature relationship using the scale context-aware module is as follows;
firstly, three groups of 1×1 convolution are utilized to respectively map feature P and concatenate results in a scale dimension, obtaining the query feature Q∈Ns×C2×H×W, the key feature K∈Ns×C2×H×W and the value feature V∈Ns×C1×H×W, wherein, C
    2
   
   =
   
    
     C
     1
    
    4
   
  
  ,, then, performing the matrix shapes transformation on Q, K, V to obtain Q′∈Ns×C2HW, K′∈Ns×C2HW and V′∈Ns×C1HW, and a scale attention map M∈NsNs is obtained through below formula:, M
   ij
  
  =
  
   
    
     Q
     i
     ′
    
    ⁢
    
     K
     j
     ′
    
   
   
    
     
      ∑
       
     
     
      j
      =
      1
     
     
      N
      s
     
    
    ⁢
    
     Q
     i
     ′
    
    ⁢
    
     K
     j
     ′
    
   
  
 

wherein i and j represent two different scales respectively, that is, the indexes of two paths, and also the row and column positions of a corresponding attention value in the scale attention map; multiplying the scale attention map M with the value feature V′ to obtain a new feature S that simultaneously perceive intra-feature and inter-feature context dependencies:

S=MV′

finally, after concatenating S and X in a channel dimension, inputting it into the classification module.