  In this paper, we describe a novel deep convolutional neural network (CNN)
that is deeper and wider than other existing deep networks for hyperspectral
image classification. Unlike current state-of-the-art approaches in CNN-based
hyperspectral image classification, the proposed network, called contextual
deep CNN, can optimally explore local contextual interactions by jointly
exploiting local spatio-spectral relationships of neighboring individual pixel
vectors. The joint exploitation of the spatio-spectral information is achieved
by a multi-scale convolutional filter bank used as an initial component of the
proposed CNN pipeline. The initial spatial and spectral feature maps obtained
from the multi-scale filter bank are then combined together to form a joint
spatio-spectral feature map. The joint feature map representing rich spectral
and spatial properties of the hyperspectral image is then fed through a fully
convolutional network that eventually predicts the corresponding label of each
pixel vector. The proposed approach is tested on three benchmark datasets: the
Indian Pines dataset, the Salinas dataset and the University of Pavia dataset.
Performance comparison shows enhanced classification performance of the
proposed approach over the current state-of-the-art on the three datasets.
