Abstract
Distortion allocation varying with wavelength in lossy compression of hyperspectral imagery is investigated, with the aim of minimizing the spectral distortion between original and decompressed data. The absolute angular error, or spectral angle mapper (SAM), is used to quantify spectral distortion, while radiometric distortions are measured by maximum absolute deviation (MAD) for near‐lossless methods, for example, differential pulse code modulation (DPCM), or mean‐squared error (MSE) for lossy methods, for example, spectral decorrelation followed by JPEG 2000. Two strategies of interband distortion allocation are compared: given a target average bit rate, distortion may be set to be constant with wavelength. Otherwise, it may be allocated proportionally to the noise level of each band, according to the virtually lossless protocol. Comparisons with the uncompressed originals show that the average SAM of radiance spectra is minimized by constant distortion allocation to radiance data. However, variable distortion allocation according to the virtually lossless protocol yields significantly lower SAM in case of reflectance spectra obtained from compressed radiance data, if compared with the constant distortion allocation at the same compression ratio.
Highlights
Hyperspectral imaging has dramatically changed the rationale of remote sensing of the Earth relying on spectral diversity
The absolute angular error, or spectral angle mapper (SAM), is used to quantify spectral distortion, while radiometric distortions are measured by maximum absolute deviation (MAD) for near-lossless methods, for example, differential pulse code modulation (DPCM), or mean-squared error (MSE) for lossy methods, for example, spectral decorrelation followed by JPEG 2000
Variable distortion allocation according to the virtually lossless protocol yields significantly lower SAM in case of reflectance spectra obtained from compressed radiance data, if compared with the constant distortion allocation at the same compression ratio
Summary
Hyperspectral imaging has dramatically changed the rationale of remote sensing of the Earth relying on spectral diversity. To meet the quality issues of hyperspectral imaging, differential pulse code modulation (DPCM) is usually employed for either lossless or near-lossless data compression The latter indicates that the decompressed data have a user-defined maximum absolute error, being zero in the lossless case. In applications of hyperspectral remote sensing, average and maximum angles between original and decompressed pixel vectors are usually adopted to measure the dissimilarity of spectra belonging to different materials. Under this perspective, spectral angle might be useful to measure the distortion of lossy compressed hyperspectral data. If more specific tasks are concerned, such as minerals identification or geological inspections, especially on coastal waters, in order to identify the presence of chlorophyll, phytoplankton, or dissolved organic materials, the high spectral resolution captured by hyperspectral instruments is beneficial
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