Abstract
Hyperspectral data compression is expected to play a crucial role in remote sensing applications. 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 based on the concept of spectral unmixing. It has been implemented on a Xilinx Virtex-II FPGA formed by several millions of gates, and with high computational power and compact size, which make this reconfigurable device very appealing for onboard, real-time data processing.
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References
Chang, C.-I.: Hyperspectral Imaging: Detection and Classification. Kluwer, New York (2003)
Motta, G., Storer, J. (eds.): Hyperspectral Data Compression. Springer, Heidelberg (2005)
Chang, C.-I., Plaza, A.: A Fast Iterative Implementation of the Pixel Purity Index Algorithm. IEEE Geoscience and Remote Sensing Letters 3, 63–67 (2006)
Du, J., Chang, C.-I.: Linear Mixture Analysis-Based Compression for Hyperspectral Image Analysis. IEEE Trans. Geoscience and Remote Sensing 42, 875–891 (2004)
Taubman, D.S., Marcellin, M.W.: JPEG2000: Image Compression Fundamentals, Standard and Practice. Kluwer, Boston (2002)
Said, A., Pearlman, W.A.: A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans. Circuits and Systems 6, 243–350 (1996)
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© 2006 Springer-Verlag Berlin Heidelberg
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Valencia, D., Plaza, A. (2006). FPGA-Based Hyperspectral Data Compression Using Spectral Unmixing and the Pixel Purity Index Algorithm. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_130
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DOI: https://doi.org/10.1007/11758501_130
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
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