Dimensionality reduction in hyperspectral image classification

Abstract

Hyperspectral images provide a vast amount of information about a scene. However, much of that information is redundant as the bands are highly correlated. For computational and data compression reasons, it is desired to reduce the dimensionality of the data set while maintaining good performance in image analysis tasks. This work presents a method of dimensionality reduction based on neural networks. A novel penalty function is presented and shown to successfully reduce the number of active neurons, which corresponds to the dimensionality of the data for the task of interest.