  Deep learning based landcover classification algorithms have recently been
proposed in literature. In hyperspectral images (HSI) they face the challenges
of large dimensionality, spatial variability of spectral signatures and
scarcity of labeled data. In this article we propose an end-to-end deep
learning architecture that extracts band specific spectral-spatial features and
performs landcover classification. The architecture has fewer independent
connection weights and thus requires lesser number of training data. The method
is found to outperform the highest reported accuracies on popular hyperspectral
image data sets.
