Improvement of the MVC-NMF Problem Using Particle Swarm Optimization for Mineralogical Unmixing of Noisy Hyperspectral Data View Full Text


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Article Info

DATE

2019-04

AUTHORS

Tohid Nouri, Majid M. Oskouei, Behrooz Alizadeh, Paolo Gamba, Andrea Marinoni

ABSTRACT

The Hyperion data are broadly available for different parts of the world. Considering the spatial resolution of Hyperion (30 × 30 m2), it is rarely possible to find a pixel consisting of only one mineral. Spectral unmixing is therefore an important procedure in which dataset pixels are demixed into various constituents. Endmember determination is the key stage in spectral unmixing. The algorithms which are not depended on the existence of pure pixels in images are more efficient particularly when the spatial resolution is low (e.g., Hyperion data). On the other hand, the lower signal-to-noise ratio of Hyperion data is a disadvantage. Minimum volume-constrained nonnegative matrix factorization (MVC-NMF) is an appropriate non-pure pixel-based algorithm in low SNR conditions. Still, MVC-NMF is based on a gradient technique and is therefore problematic in the case of large amount of data. Particle swarm optimization (PSO) is a metaheuristic algorithm and computational simplicity is its main advantage. The minimum volume-constrained version of PSO (MVC-PSO) was then investigated on Western Ardabil Hyperion data and the results were compared with MVC-NMF. To validate the accuracy of the results, 20 surface samples were collected and analyzed by spectrometry and X-ray diffraction (XRD). Measured spectra by analytical spectral devices Inc. FieldSpec were used to create a native spectral library. Native spectra as well as United States Geological Survey mineral spectral library were applied for identification of unknown endmembers spectra. The XRD results were implemented for quantitative validation of abundances maps of endmembers using Average Abundance Ratio. More... »

PAGES

541-550

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12524-018-0885-9

DOI

http://dx.doi.org/10.1007/s12524-018-0885-9

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https://app.dimensions.ai/details/publication/pub.1107888761


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