Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs
Algorithm 1
SS-MNF-ARMG.
Input: the original HSI set is ; the training set is ; the test set is ; the number of spectra after dimension reduction is ; the patch size for FE is , whereis odd and ;
Output: the best classification result of all test samples;
(1)
for each do
(2)
Obtain the dimension-reduced HSI by using MNF;
(3)
Extract the spectral vector of ;
(4)
for each do
(5)
Calculate the spatial vector by using LBP;
(6)
Obtain the spectral-spatial vector by stacking the and the ;
(7)
Obtain the best (overall accuracy) by voting, and the corresponding confusion matrix ;
(8)
end for
(9)
Obtain the best from by decision fusion, and the corresponding confusion matrix ;
