Minimum volume simplicial enclosure for spectral unmixing of remotely sensed hyperspectral data

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) endmember abundance estimation in mixed pixels. One of the main problems concerning the identification of spectral endmembers is the lack of pure spectral signatures in real hyperspectral data due to spatial resolution and mixture phenomena happening at different scales. In this paper, we present a new method for endmember estimation which does not assume the presence of pure pixels in the input data. The method minimizes the volume of an enclosing simplex in the reduced space while estimating the fractional abundance of vertices in simultaneous fashion, as opposed to other volume-based approaches such as N-FINDR which inflate the simplex of maximum volume that can be formed using available image pixels. Our experimental results and comparisons to other endmember extraction al...

• Type: Journal
• Year: 2010
• Where: IGARSS
• Authors: Eligius M. T. Hendrix, Inmaculada García, Javier Plaza, Antonio J. Plaza
• Keywords: Fractional Abundances, Hyperspectral Data, IGARSS 2010, Real Hyperspectral Data, Remote Sensing