Hyperspectral Unmixing by Nuclear Norm Difference Maximization based Dictionary Pruning

TL;DR

This paper introduces a novel hyperspectral unmixing method that uses nuclear norm difference maximization for dictionary pruning, effectively selecting spectral library elements that best represent the data.

Contribution

It proposes a new nuclear norm difference based approach leveraging low rank properties for more effective dictionary pruning in hyperspectral unmixing.

Findings

Effective in synthetic data scenarios

Validated on real hyperspectral images

Improves endmember selection accuracy

Abstract

Dictionary pruning methods perform unmixing by identifying a smaller subset of active spectral library elements that can represent the image efficiently as a linear combination. This paper presents a new nuclear norm difference based approach for dictionary pruning utilizing the low rank property of hyperspectral data. The proposed workflow calculates the nuclear norm of abundance of the original data assuming the whole spectral library as endmembers. In the next step, the algorithm calculates nuclear norm of abundance after appending a spectral library element with the data. The spectral library elements having the maximum difference in the nuclear norm of the obtained abundance matrices are suitable candidates for being image endmember. The proposed workflow is verified with a large number of synthetic data generated by varying condition as well as some real images.