Futuristic Greedy Approach To Sparse Unmixing Of Hyperspectral Data

NAVEED AKHTAR, Faisal Shafait, Ajmal Saeed Mian

Abstract:

Spectra measured at a single pixel of a remotely
sensed hyperspectral image is usually a mixture of multiple spec-
tral signatures (endmembers) corresponding to different materials
on the ground. Sparse unmixing assumes that a mixed pixel is
a sparse linear combination of different spectra already avail-
able in a spectral library. It uses sparse approximation (SA)
techniques to solve the hyperspectral unmixing problem. Among
these techniques, greedy algorithms suite well to sparse unmix-
ing. However, their accuracy is immensely compromised by the
high correlation of the spectra of different materials. This paper
proposes a novel greedy algorithm, called OMP-Star, that shows
robustness against the high correlation of spectral signatures. We
preprocess the signals with spectral derivatives before they are
used by the algorithm. To approximate the mixed pixel spectra, the
algorithm employs a futuristic greedy approach that, if necessary,
considers its future iterations before identifying an endmember.
We also extend OMP-Star to exploit the nonnegativity of spectral
mixing. Experiments on simulated and real hyperspectral data
show that the proposed algorithms outperform the state-of-the-art
greedy algorithms. Moreover, the proposed approach achieves re-
sults comparable to convex relaxation-based SA techniques, while
maintaining the advantages of greedy approaches