Field Programmable Gate Arrays (FPGA) for Pixel Purity Index Using Blocks of Skewers for Endmember Extraction in Hyperspectral Imagery

Abstract

Pixel purity index (PPI) is a popular endmember extraction algorithm because of its availability in the ENVI software. Recently, its hardware implementation has been explored by the so-called block of skewers (BOS) that can be used to generate desired skewers required by the PPI with significantly reduced computational complexity. This paper investigates the feasibility of field programmable gate arrays (FPGAs) design for the BOS of a modified PPI (called automatic PPI), referred to as BOS-APPI in high speed computation. Several block designs based on cube, pyramid, square and tetrahedron are also developed for the BOS-APPI along with their FPGA implementations as new reconfigurable systems to perform fast and onboard processing of hyperspectral imagery. Finally, the proposed FPGA designs are realized and substantiated by simulations.

Keywords

automatic pixel purity index (APPI)block of skewers (BOS)-APPI field programmable gate arrays (FPGA)pixel purity index (PPI)virtual dimensionality (VD)

Get full access to this article

View all access options for this article.

References

Boardman, J.W. (1994). Geometric mixture analysis of imaging spectrometry data, Proc. Int. Geoscience and Remote Sensing Symp., Vol. 4, pp. 2369-2371.

Chang, C.-I. (2003). Hyperspectral imaging: Techniques for spectral detection and classification, Kluwer Academic/ Plenum Publishers .

Chang, C.-I. , Wu, C.-C. , Liu, W. , and Ouyang, C.Y. (2006). A growing simplex method for endmember extraction , IEEE Trans. on Geoscience and Remote Sensing 44(10): 2804- 2819.

Chang, C.-I. , Hsueh, M. , Chaudhry, F. , Liu, W. , Wu, C.-C. , Solya, G. , and Plaza, A. (2007). A pyramid-based block of skewers for pixel purity index for endmember extraction in hyperspectral imagery, Internat. J. High Speed Electronics and Systems.

Chaudhary, F.A. (2005). Pixel purity index-based endmember extraction for hyperspectral data exploitation, MS thesis, Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County.

Green, A.A. , Berman, M. , Switzer, P. , and Craig, M.D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Trans. on Geosciences and Remote Sensing 26: 65-74.

Harsanyi, J. and Chang, C.-I. (1994). Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach, IEEE Trans. on Geoscience and Remote Sensing 32(4): 779-785.

Lavenier, D. , Theiler, J. , Szymanski, J. , Gokhle, M. and Frigo, J. (2000). "FPGA Implementation of Pixel Purity Index Method ," Proc. SPIE, vol. 4212, pp. 30-41.

Palnitkar, S. (2003). Verilog HDL: a guide to digital design and synthesis , SunSoft Press.

10.

Theiler, J. and Lavenier, D. , Harvey, N.R. , Perkins, S. and Szymanski, J. (2000) "Using blocks of skewers for fast computation of pixel purity index" Proc. SPIE, vol. 4132, pp. 61-71.

11.

Valencia, D. , Plaza, A. , Vega-Rodriguez, M.A. , and Perez, R.M. (2005). FPGA design and implementation of a fast pixel purity index algorithm for endmember extraction in hyperspectral imagery, SPIE East, Boston.