  Traditional Active/Self/Interactive Learning for Hyperspectral Image
Classification (HSIC) increases the size of the training set without
considering the class scatters and randomness among the existing and new
samples. Second, very limited research has been carried out on joint
spectral-spatial information and finally, a minor but still worth mentioning is
the stopping criteria which not being much considered by the community.
Therefore, this work proposes a novel fuzziness-based spatial-spectral within
and between for both local and global class discriminant information preserving
(FLG) method. We first investigate a spatial prior fuzziness-based
misclassified sample information. We then compute the total local and global
for both within and between class information and formulate it in a
fine-grained manner. Later this information is fed to a discriminative
objective function to query the heterogeneous samples which eliminate the
randomness among the training samples. Experimental results on benchmark HSI
datasets demonstrate the effectiveness of the FLG method on Generative, Extreme
Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL
classifiers.
