Journal of VLSI Signal Processing Systems - Special issue: application specific array processors
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Hyperspectral Image Compression on Reconfigurable Platforms
FCCM '02 Proceedings of the 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
64-bit floating-point FPGA matrix multiplication
Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays
Hyperspectral Data Compression
Hyperspectral Data Compression
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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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.