Name
Title | ||
|---|---|---|
Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction. |
Abstract | ||
|---|---|---|
This paper presents a novel scheme for lossless/nearlossless hyperspectral image compression, that exploits a classified spectral prediction. MMSE spectral predictors are calculated for small spatial blocks of each band and are classified (clustered) to yield a user-defined number of prototype predictors for each wavelength, capable of matching the spatial features of different classes of pixel spectra. Unlike most of the literature, the proposed method employs a purely spectral prediction, that is suitable for compressing the data in band-interleaved-by-line (BIL) format, as they are available at the output of the on-board spectrometer. In that case, the training phase, i.e., clustering of predictors for each wavelength, may be moved off-line. Thus, prediction will be slightly less fitting, but the overhead of predictors calculated on-line is saved. Although prediction is purely spectral, hence 1D, spatial correlation is removed by the training phase of predictors, aimed at finding statistically homogeneous spatial classes matching the set of prototype spectral predictors. Experimental results on AVIRIS data show improvements over the most advanced methods in the literature, with a computational complexity far lower than that of analogous methods by other authors. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/IGARSS.2005.1526122 | IGARSS |
Keywords | Field | DocType |
data compression,decorrelation,feature extraction,geophysical signal processing,geophysical techniques,image classification,image coding,linear predictive coding,multidimensional signal processing,pattern clustering,remote sensing,spectral analysis,band-interleaved-by-line format,classified linear spectral prediction,hyperspectral imagery,image classification,image compression,low-complexity lossless compression,near-lossless compression,pattern clustering,pixel spectra,spatial feature matching,spectrometer | Computer vision,Entropy encoding,Spatial correlation,Pattern recognition,Computer science,Hyperspectral imaging,Artificial intelligence,Cluster analysis,Data compression,Image compression,Linear predictive coding,Lossless compression | Conference |
Volume | Citations | PageRank |
1 | 6 | 0.54 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Aiazzi | 1 | 275 | 27.84 |
| Stefano Baronti | 2 | 559 | 50.87 |
| Cinzia Lastri | 3 | 50 | 4.28 |
| Leonardo Santurri | 4 | 25 | 5.14 |
| Luciano Alparone | 5 | 901 | 80.27 |