  In this paper, a novel statistical metric learning is developed for
spectral-spatial classification of the hyperspectral image. First, the standard
variance of the samples of each class in each batch is used to decrease the
intra-class variance within each class. Then, the distances between the means
of different classes are used to penalize the inter-class variance of the
training samples. Finally, the standard variance between the means of different
classes is added as an additional diversity term to repulse different classes
from each other. Experiments have conducted over two real-world hyperspectral
image datasets and the experimental results have shown the effectiveness of the
proposed statistical metric learning.
