Abstract
The next generation of satellite-based remote sensing instruments will produce an unprecedented volume of data. Imaging spectrometers, also known as hyperspectral imagers, are prime examples. They collect image data in hundreds of spectral bands simultaneously from the near ultraviolet through the short wave infrared, and are capable of providing direct identification of surface materials. A schematic diagram illustrating the concept of an imaging spectrometer is given in Fig.11. The volume and complexity of data produced by these instruments offers a significant challenge to downlink transmission and traditional image analysis methods. Since they produce 3-dimensional (3D) data cubes in which two dimensions correspond to spatial position and the third to wavelength, raw data rates can easily exceed the available downlink capacity or on-board storage capacity. Often, therefore, a portion of the data collected on board is discarded before transmission. This data reduction process may involve: 1) reducing the duty cycle, 2) reducing the spatial or spectral resolution, and 3) reducing the spatial or spectral range. Obviously, in such cases large amounts of information are lost.
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Qian, SE., Hollinger, A.B., Williams, D., Manak, D. (1997). 3D Data Compression Systems Based on Vector Quantization for Reducing the Data Rate of Hyperspectral Imagery. In: Lampropoulos, G.A., Lessard, R.A. (eds) Applications of Photonic Technology 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9250-8_100
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DOI: https://doi.org/10.1007/978-1-4757-9250-8_100
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