We proposes a simple deep learning architecture combining elements of
Inception, ResNet and Xception networks. Four new datasets were used for
classification with both small and large training samples. Results in terms of
classification accuracy suggests improved performance by proposed architecture
in comparison to Bayesian optimised 2D-CNN with small training samples.
Comparison of results using small training sample with Indiana Pines
hyperspectral dataset suggests comparable or better performance by proposed
architecture than nine reported works using different deep learning
architectures. In spite of achieving high classification accuracy with limited
training samples, comparison of classified image suggests different land cover
classes are assigned to same area when compared with the classified image
provided by the model trained using large training samples with all datasets.