  Convolutional Neural Networks (CNN) have been rigorously studied for
Hyperspectral Image Classification (HSIC) and are known to be effective in
exploiting joint spatial-spectral information with the expense of lower
generalization performance and learning speed due to the hard labels and
non-uniform distribution over labels. Several regularization techniques have
been used to overcome the aforesaid issues. However, sometimes models learn to
predict the samples extremely confidently which is not good from a
generalization point of view. Therefore, this paper proposed an idea to enhance
the generalization performance of a hybrid CNN for HSIC using soft labels that
are a weighted average of the hard labels and uniform distribution over ground
labels. The proposed method helps to prevent CNN from becoming over-confident.
We empirically show that in improving generalization performance, label
smoothing also improves model calibration which significantly improves
beam-search. Several publicly available Hyperspectral datasets are used to
validate the experimental evaluation which reveals improved generalization
performance, statistical significance, and computational complexity as compared
to the state-of-the-art models. The code will be made available at
https://github.com/mahmad00.
