Dimensionality Reduction & Classification of hyperspectral images using Deep Learning

Abstract

Hyperspectral image classification is a widely used technique for the analysis of remotely sensed images, due to the rich spatial and spectral information the hyperspectral images provide. Therefore, they are used in many real world applications. However, hyperspectral images classification can be a challenging task. On one hand, the large number of spectral bands yields to a complexity of the system. On the other hand, the task may result in a poor performance due to the high inter-class similarity and high intra-class variability. Towards this direction, we proposed deep learning methods to extract the most important features from the hyperspectral images. We focused on the convolutional neural networks because they have the ability to learn high-level spatial and spectral features; they process the input data to generate the classification outcomes. The experiments have been performed on four datasets, namely: Indian Pines, Salinas Scene, Pavia University, and Kennedy Space Center. The results obtained using the CNN algorithms were considerably high, and were predominant to other machine learning and deep learning algorithms.