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Title: FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data
Authors: Gonzalez, Carlos
Lopez, Sebastian 
Mozos, Daniel
Sarmiento, Roberto 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hyperspectral imaging
Field programmable gate arrays
Linear systems
Issue Date: 2015
Publisher: 1939-1404
Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 
Conference: 6th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)
Abstract: Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures (endmembers) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular and widely used techniques for this purpose is the HySime algorithm but, due to the complexity and high dimensionality of hyperspectral scenes, this technique is computational expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HySime algorithm. Our system includes a direct memory access (DMA) module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASAs airborne visible infra-red imaging spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World trade center (WTC) in New York. Experimental results demonstrate that our hardware version of the HySime algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
ISSN: 1939-1404
DOI: 10.1109/JSTARS.2015.2425731
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing[ISSN 1939-1404],v. 8 (7112455), p. 2870-2883
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