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Design and analysis of real-time endmember extraction algorithms for hyperspectral imagery

Endmember extraction has recently received considerable attention in hyperspectral data exploitation since they represent crucial and vital information for hyperspectral data analysis. So far, no work has been reported on how to implement endmember extraction algorithms in real-time. An endmember is defined as an idealized signature and may or may not exist as a data sample or an image pixel. The interest of endmember extraction arises in the use of hundreds of contiguous spectral channels that allows a hyperspectral imaging sensor to uncover many subtle substances in diagnostic bands. However, finding such substances also presents a great challenge to hyperspectral data analysts and becomes imperative when it comes to satellite communication if a hyperspectral imaging sensor is operated in space platform where bandwidths used by satellite links may be very limited and downloading all the data may not be realistic in many practical applications. In order to address this need many endmember extraction algorithms have been developed and designed in the past, but no work has been reported on how to implement endmember extraction algorithms in real-time. This paper investigates this issue in designing algorithms for real time processing of endmember extraction and developed several endmember extraction algorithms derived from the widely used N-finder algorithm (NFINDR) that can be implemented in real time.