AN INVESTIGATION ON SELF-NORMALIZED DEEP NEURAL NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Computational advances have allowed for the development of deep learning (DL) applied to remote sensing data and, particularly, to hyperspectral image (HSI) classification. Deeper architectures are able to establish a better separation of the characteristics of the data, allowing for a better and accurate performance. However, it is known that employing very deep architectures with many abstraction levels can result in a loss of information due to the fact that deep networks often normalize each data individually, without considering the set of adjacent data. To address this issue, this paper implements a self-normalizing neural network (SNN) in order to extract high-level abstract representations without losing information due to the data initialization. The selected activation function (scaled exponential linear units or SELU) normalizes the data considering their neighborhood's information and a special dropout technique (alpha-dropout), obtaining good classification performance while maintaining the data characteristics across the successive layers. Obtained results show that the proposal improves the performance with few training samples.