A Comparison Study of ORASIS and VCA for Mineralogical Unmixing of Hyperspectral Data
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RESEARCH ARTICLE
A Comparison Study of ORASIS and VCA for Mineralogical Unmixing of Hyperspectral Data Tohid Nouri 1 & Majid M. Oskouei 1 & Hamid Zekri 1
Received: 24 December 2014 / Accepted: 27 December 2015 # Indian Society of Remote Sensing 2016
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.
* Majid M. Oskouei [email protected]
1
Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran
The results highly correspond to the geological, geochemical and mineralogical reports of both regions. Keywords Spectral unmixing . Hyperspectral remote sensing . Endmember determination . Non-pure pixel based algorithms . Orasis . VCA
Introduction The concept of hyperspectral remote sensing was first introduced in the mid-eightieth decade and since then has been mostly used for mineral mapping. Considering the existing spatial resolutions, hyperspectral dataset pixels are usually composed of different pure materials known as endmembers. In the mineral exploration related to hyperspectral data analysis, the main purpose is to decompose pixels into pure constituents throughout a process called spectral unmixing. In the spectral unmixing procedure, the phase of endmember extraction is the most challenging stage also one of the significant factors enhancing the accuracy of unmixing process (Miao and Qi 2007; Cui et al. 2011). However, many endmember extraction algorithms based on a pure pixel assumption such as Vertex Component Analysis (VCA) (Nascimento and Dias 2005), N-finder (Winter 1999), Pixel Purity Index (PPI) (Boardman et al. 1995), and Iterative Error Analysis
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