  Hyper-spectral satellite imagery is now widely being used for accurate
disaster prediction and terrain feature classification. However, in such
classification tasks, most of the present approaches use only the spectral
information contained in the images. Therefore, in this paper, we present a
novel framework that takes into account both the spectral and spatial
information contained in the data for land cover classification. For this
purpose, we use the Gaussian Maximum Likelihood (GML) and Convolutional Neural
Network methods for the pixel-wise spectral classification and then, using
segmentation maps generated by the Watershed algorithm, we incorporate the
spatial contextual information into our model with a modified majority vote
technique. The experimental analyses on two benchmark datasets demonstrate that
our proposed methodology performs better than the earlier approaches by
achieving an accuracy of 99.52% and 98.31% on the Pavia University and the
Indian Pines datasets respectively. Additionally, our GML based approach, a
non-deep learning algorithm, shows comparable performance to the
state-of-the-art deep learning techniques, which indicates the importance of
the proposed approach for performing a computationally efficient classification
of hyper-spectral imagery.
