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Keywords
(11)
Compression Algorithm
Compression Ratio
Data Compression
hyperspectral data
Indexing Terms
Infrared Imaging
Near Infrared
Reference Systems
Spatial Pattern
Vector Quantizer
Compact Airborne Spectrographic Imager
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Assessing the Performance Effects of Data Compression for SAR Imagery
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Effect of lossy vector quantization hyperspectral data compression on retrieval of rededge indices
Effect of lossy vector quantization hyperspectral data compression on retrieval of rededge indices,10.1109/36.934077,IEEE Transactions on Geoscience
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Effect of lossy vector quantization hyperspectral data compression on retrieval of rededge indices
(
Citations: 6
)
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ShenEn Qian
,
Allan B. Hollinger
,
Melanie Dutkiewicz
,
Herbert Tsang
,
Harold Zwick
,
James R. Freemantle
This paper evaluates lossy vector quantizationbased
hyperspectral data
compression algorithms, using rededge indices as endproducts. Three
compact airborne spectrographic imager
(CASI) data sets and one airborne visible/infrared
imaging spectrometer
(AVIRIS) data set from vegetated areas were tested. A basic compression system for
hyperspectral data
called the “reference” system, and threespeed improved compression systems called systems 1, 2, and 3, respectively, were examined. Five rededge products representing the
near infrared
(NIR) reflectance shoulder (Vog 1), the NIR reflectance maximum (Red rs), the difference between the reflectance maximum and the minimum (Red rd), the wavelength of the reflectance maximum (Red lo), and the wavelength of the point of inflection of the NIR vegetation reflectance curve (Red lp) were retrieved from each original data set and from their decompressed data sets. The experiments show that the reference system induces the smallest product errors of the four compression systems. System 1 and 2 perform fairly closely to the reference system. They are the recommended compression systems since they compress a data set hundreds of times faster than the reference system. System 3 performs similarly to the reference system at high compression ratios. Product errors increase with the increase of compression ratio. The overall product errors are dominated by Vog 1, Red rs, and Red rd, since the amplitude of product error for these products is over one order of magnitude greater than those for the Redlo and Red lp products. The difference between the overall error from the reference and that from system 1 or 2 is below 0.5% at all compression ratios. The overall product error induced by system 1 or 2 is below 3.0% and 2.0% for CASI and AVIRIS data sets, respectively, when the
compression ratio
is 100 and below. Spatial patterns of the product errors were examined for tha AVRIS data set. For all products, the errors are uniformly distributed in vegetated areas. Errors are relatively high in nonvegeted and mixedpixel areas
Journal:
IEEE Transactions on Geoscience and Remote Sensing  IEEE TRANS GEOSCI REMOT SEN
, vol. 39, no. 7, pp. 14591470, 2001
DOI:
10.1109/36.934077
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Citation Context
(4)
...To strengthen the motivation of lossy compression, it has been found that in many applications a moderate amount of information loss does not significantly impair data exploitation (e.g., see [9], [12], and [
14
]); however, the suitability of lossy compression should be assessed for each specific remote sensing application, while in the present work we only consider general error metrics based on the mean squarederror...
Barbara Penna
,
et al.
Progressive 3D coding of hyperspectral images based on JPEG 2000
...Extensive studies of the impact of VQ based data compression on hyperspectral imagery for remote sensing applications have been reported [
15
]–[20]...
ShenEn Qian
.
Hyperspectral data compression using a fast vector quantization algori...
...These metrics include those that signify scientific loss for end users [
42
, 45], contentindependent metrics 76 HYPERSPECTRAL DATA COMPRESSION...
Bormin Huang
,
et al.
Lossless Compression of Ultraspectral Sounder Data
...These metrics include those that signify scientific loss for end users (
Qian et al. 2001;
Ryan et al. 1998), contentindependent metrics (Shen et al. 1993), and even visual comparisons (Eckstein et al. 2000)...
Bormin Huang
,
et al.
P1.35 ADVANCES IN COMPRESSION OF ULTRASPECTRAL SOUNDER DATA
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Citations
(6)
Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction
(
Citations: 2
)
Bormin Huang
,
Alok Ahuja
,
HungLung Huang
Journal:
Journal of Atmospheric and Oceanic Technology  J ATMOS OCEAN TECHNOL
, vol. 25, no. 6, 2008
Ultraspectral sounder data compression review
Bormin Huang
,
Hunglung Huang
Journal:
Frontiers of Earth Science in China
, vol. 2, no. 4, pp. 487501, 2008
Progressive 3D coding of hyperspectral images based on JPEG 2000
(
Citations: 44
)
Barbara Penna
,
Tammam Tillo
,
Enrico Magli
,
Gabriella Olmo
Journal:
IEEE Geoscience and Remote Sensing Letters  IEEE GEOSCI REMOTE SENS LETT
, vol. 3, no. 1, pp. 125129, 2006
Hyperspectral data compression using a fast vector quantization algorithm
(
Citations: 16
)
ShenEn Qian
Journal:
IEEE Transactions on Geoscience and Remote Sensing  IEEE TRANS GEOSCI REMOT SEN
, vol. 42, no. 8, pp. 17911798, 2004
Lossless Compression of Ultraspectral Sounder Data
(
Citations: 8
)
Bormin Huang
,
Alok Ahuja
,
HungLung Huang