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.

Getting started with deep learning has become very simple and convenient, all thanks to wonderful duo of keras and tensorflow. You just need to do some imports, define some layers and bingo, you have your deep learning architecture ready to be trained and eventually give some amazing results. Keras has made such an amazing abstraction that even a total stranger to the topic as well can start training their own deep learning models. However if you are calling yourself a Data Scientist/Machine Learning Engineer then having some basic understanding of what's happening under the hood is a must, I am not saying you need to exactly know the hundreds of lines of code behind it but at least have a some understanding what those lines of code are doing. Without wasting anytime lets dive into some of the most commonly used keras components and try to understand them piece by piece, this will involve a basic understanding of Object Oriented Programming but don't worry I will try to keep it as simple as possible.