Towards Real-Time Compression of Hyperspectral Images Using Virtex-II FPGAs

Abstract

Hyperspectral imagery is a new type of high-dimensional image data which is now used in many Earth-based and planetary exploration applications. Many efforts have been devoted to designing and developing compression algorithms for hyperspectral imagery. Unfortunately, most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique which relies on the concept of spectral unmixing, one of the most popular approaches to deal with mixed pixels and subpixel targets in hyperspectral analysis. The proposed method uses a two-stage approach in which the purest pixels in the image (endmembers) are first extracted and then used to express mixed pixels as linear combinations of end-members. The result is an intelligent, applicationbased compression technique which has been implemented and tested on a Xilinx Virtex-II FPGA.