Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension

TL;DR

This paper presents a feature selection method for hyperspectral data that minimizes a tight VC dimension bound, leading to more efficient and accurate classification by selecting a sparse subset of spectral bands.

Contribution

It introduces a VC dimension-based filter feature selection algorithm tailored for hyperspectral data, outperforming existing methods in sparsity and accuracy.

Findings

The method achieves higher classification accuracy than state-of-the-art techniques.

It produces sparser feature sets, reducing computational complexity.

Demonstrated effectiveness on widely used hyperspectral datasets.

Abstract

Hyperspectral data consists of large number of features which require sophisticated analysis to be extracted. A popular approach to reduce computational cost, facilitate information representation and accelerate knowledge discovery is to eliminate bands that do not improve the classification and analysis methods being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the specifics of the classification method being used. This paper employs a recently proposed filter-feature-selection algorithm based on minimizing a tight bound on the VC dimension. We have successfully applied this algorithm to determine a reasonable subset of bands without any user-defined stopping criteria on widely used hyperspectral images and demonstrate that this method outperforms state-of-the-art methods in terms of both sparsity of feature set as well as accuracy of classification.\end{abstract}