Comparative Study and Analysis of Dimensionality Reduction Techniques for Hyperspectral Data

Abstract

The enhanced capabilities of the remote sensing devices lead to capture more precise and accurate spatial and spectral information about surface materials. Increased spectral resolution results in more number of spectral bands and raises the challenge of data dimensionality. This high volume data holds plenty of redundant information. This redundancy affects both the time as well as space complexity of the system. To process and analyse the hyperspectral data with less computational cost with no information loss, data dimensionality needs to be reduced. The literature shows that the traditional image processing techniques with some modifications are applied for hyperspectral dimensionality reduction, but none of the methods give specific solution. This paper evaluates the performances and limitations of the state-of-the-art dimensionality reduction techniques. The algorithms studied and evaluated are Principal Components Analysis, Independent Component Analysis, Minimum Noise Fraction, Fisher Linear Discriminant Analysis, Factor Analysis and Linear Discriminant Analysis. The experiments are performed on the Indian Pines AVIRIS & Gulbarga Subset (AVIRIS-NG) hyperspectral datasets.