>
Fa   |   Ar   |   En
   Randomized SVD methods in hyperspectral imaging  
   
نویسنده zhang j. ,erway j. ,hu x. ,zhang q. ,plemmons r.
منبع journal of electrical and computer engineering - 2012 - شماره : 0
چکیده    We present a randomized singular value decomposition (rsvd) method for the purposes of lossless compression,reconstruction,classification,and target detection with hyperspectral (hsi) data. recent work in low-rank matrix approximations obtained from random projections suggests that these approximations are well suited for randomized dimensionality reduction. approximation errors for the rsvd are evaluated on hsi,and comparisons are made to deterministic techniques and as well as to other randomized low-rank matrix approximation methods involving compressive principal component analysis. numerical tests on real hsi data suggest that the method is promising and is particularly effective for hsi data interrogation. © 2012 jiani zhang et al.
آدرس department of mathematics,wake forest university,winston-salem, United States, department of mathematics,wake forest university,winston-salem, United States, department of mathematics,wake forest university,winston-salem, United States, department of biostatistical sciences,wake forest school of medicine,winston-salem, United States, departments of mathematics and computer science,wake forest university,winston-salem, United States
 
     
   
Authors
  
 
 

Copyright 2023
Islamic World Science Citation Center
All Rights Reserved