A novel FPGA-based architecture for the estimation of the virtual dimensionality in remotely sensed hyperspectral images
- 369 Downloads
A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular ways to determine the number of endmembers is by estimating the virtual dimensionality (VD) of the hyperspectral image using the well-known Harsanyi–Farrand–Chang (HFC) method. Due to the complexity and high dimensionality of hyperspectral scenes, this task is computationally 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 HFC-VD algorithm. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA’s Airborne Visible Infra-Red Imaging Spectrometer over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the HFC-VD algorithm can significantly outperform an equivalent software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Most important, our implementation exhibits real-time performance with regard to the time that the hyperspectral instrument takes to collect the image data.
KeywordsNumber of endmembers estimation Hyperspectral imaging Field-programmable gate arrays (FPGAs) Virtual dimensionality Reconfigurable hardware
This work has been supported by the by the Spanish Ministry of Science and Innovation under references READAR (TIN2013-40968-P) and DREAMS (TEC2011-28666-C04-04).
- 3.Harsanyi, J. C., Farrand, W., Chang, C.-I.: Detection of subpixel spectral signatures in hyperspectral image sequences. In: Proceedings of the American Society for Photogrammetry and Remote Sensing, Annual Meeting, pp. 236–247 (1994)Google Scholar
- 4.Patel, A., Kosko, B.: Optimal noise benefits in Neyman–Pearson signal detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008, pp. 3889–3892, (2008)Google Scholar
- 7.Plaza, A., Valencia, D., Plaza, J.: High-performance computing in remotely sensed hyperspectral imaging: the pixel purity index algorithm as a case study. In: Proceedings of international parallel and distributed processing symposium (IPDPS) (2006)Google Scholar
- 10.Bernabe, S., Lopez, S., Plaza, A., Sarmiento, R., Rodriguez, P. G.: FPGA design of an automatic target generation process for hyperspectral image analysis. In: IEEE international conference on parallel and distributed systems (ICPADS) 2011, pp. 1010–1015, 7–9 (Dec. 2011)Google Scholar
- 14.Gonzalez, C., Resano, J., Mozos, D., Plaza, A., Valencia, D.: FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis. EURASIP J. Adv. Signal Process., article 969806 (2010)Google Scholar
- 19.Quarteroni, A., Sacco, R., Saleri, F.: Numerical Mathematics, 2nd edn. Texts in Applied Mathematics, vol. 37. Springer, New York (2007)Google Scholar
- 20.Davidson, C. E., Ben-David, A.: On the use of covariance and correlation matrices in hyperspectral detection. Appl Image. Pattern Recogn. Works. (AIPR), pp. 1–6 (2011)Google Scholar