A Comparison Study of ORASIS and VCA for Mineralogical Unmixing of Hyperspectral Data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-10

AUTHORS

Tohid Nouri, Majid M. Oskouei, Hamid Zekri

ABSTRACT

Hyperspectral datasets are usually composed of different pure materials or endmembers. Endmember extraction is the most challenging stage in the spectral unmixing procedure. In the case of mineral detection studies, due to the absence of pure pixels for some constituent minerals, the common methods based on pure pixel assumption do not yield accurate results. The current study aims to evaluate the efficiency of ORASIS in endmember determination. ORASIS is a collection of stepwise algorithms working together to produce a set of endmembers not necessarily included in the dataset. The ORASIS method was applied to estimate the endmembers of three hyperspectral dataset series: 1) highly mixed synthetic dataset with maximum purity of 0.48 %, 2) AVIRIS dataset of Cuprite, Nevada, 3) Hyperion dataset of the Dost-Bayli area, Ardabil. SAM and SFF techniques were used to identify the unknown endmembers by comparison of them with USGS spectral library. The functionality of the VCA and PPI as pure pixel-based approaches were also investigated along with ORASIS. The number of estimated endmembers of ORASIS that were appropriately matched with reference spectra was more than VCA and PPI so that majority of detected minerals are present in the study areas. So, it can be inferred that ORASIS generally outperforms the VCA and PPI. The Linear Spectral Unmixing algorithm was implemented for demixing of the Cuprite and Dost-Bayli datasets; and abundances maps were generated using mapping methods. The results highly correspond to the geological, geochemical and mineralogical reports of both regions. More... »

PAGES

723-733

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12524-015-0546-1

DOI

http://dx.doi.org/10.1007/s12524-015-0546-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011446192


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