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:: Volume 6, Issue 3 (12-2018) ::
jgit 2018, 6(3): 93-114 Back to browse issues page
A band selection technique for optimized hyperspectral unmixing
Omid Ghaffari * , Mohammad Javad Valadan Zoej , Mehdi Mokhtarzade
K.N.Toosi University of Technology
Abstract:   (2967 Views)
Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub pixel composition of spectral mixtures. The mathematical solution of the mixing problem is to resolve a set of linear equations using least squares approaches. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcome of conventional SMA approaches applying a linear mixture model using a set of fixed endmembers. Also, if endmembers are highly correlated, the matrix will become non-orthogonal, the inversion will be unstable and the inverse or estimated fractions will become highly sensitive to random errors (e.g., noise). In this paper, we present a new band selection method that comprises a band prioritization and a band de-correlation. The band prioritization will prioritizes all bands according to the reduced spectral variability of endmembers which will be used for unmixing. Bands are then selected on the basis of their associated priorities. Since the band prioritization does not consider as spectral correlation, a band de-correlation using the angles between bands are being applied to de-correlate prioritized bands. It is shown that the proposed band selection method effectively eliminates a great number of insignificant bands. Surprisingly, the experimental results on real and synthetic data sets show that with a proper band selection less than 0.2 of the total number of bands can achieve comparable performance using all bands.
Keywords: Hyperspectral Images, Unmixing, Band selection, Spectral Variability, Similarity Measures.
Full-Text [PDF 1807 kb]   (1198 Downloads)    
Type of Study: Research | Subject: Aerial Photogrammetry
Received: 2018/12/25 | Accepted: 2018/12/25 | Published: 2018/12/25
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Ghaffari O, Valadan Zoej M J, Mokhtarzade M. A band selection technique for optimized hyperspectral unmixing. jgit 2018; 6 (3) :93-114
URL: http://jgit.kntu.ac.ir/article-1-619-en.html

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Volume 6, Issue 3 (12-2018) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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