AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data

scholarly journals Spectral Distortion in Lossy Compression of Hyperspectral Data

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Bruno Aiazzi ◽  
Luciano Alparone ◽  
Stefano Baronti ◽  
Cinzia Lastri ◽  
Massimo Selva
Keyword(s):  
Mean Squared Error ◽  
Rate Distortion ◽  
Lossy Compression ◽  
Hyperspectral Data ◽  
Spectral Distortion ◽  
Jpeg 2000 ◽  
Pulse Code Modulation ◽  
Spectral Angle Mapper ◽  
Differential Pulse Code Modulation ◽  
Radiance Data

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 ofinterbanddistortion 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 thevirtually losslessprotocol. 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.

Unsupervised classification-based hyperspectral data processing: lossy compression

2018 ◽  
Vol 50 (12) ◽  
Author(s):  
Xiao-Yu Cheng ◽  
Yue-Ming Wang ◽  
Ran Guo ◽  
Jun-Ze Huang
Keyword(s):  
Data Processing ◽  
Unsupervised Classification ◽  
Lossy Compression ◽  
Hyperspectral Data

Quality issues for hyperspectral data dissemination: radiometric and spectral distortions in lossy compression

2003 ◽  
Author(s):  
Bruno Aiazzi ◽  
Luciano Alparone ◽  
Stefano Baronti ◽  
Franco Lotti ◽  
Leonardo Santurri ◽  
...  
Keyword(s):  
Data Dissemination ◽  
Lossy Compression ◽  
Hyperspectral Data ◽  
Quality Issues

VST-based lossy compression of hyperspectral data for new generation sensors

2013 ◽  
Author(s):  
Alexander N. Zemliachenko ◽  
Ruslan A. Kozhemiakin ◽  
Mykhail L. Uss ◽  
Sergey K. Abramov ◽  
Vladimir V. Lukin ◽  
...  
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data ◽  
New Generation

Lossy compression of hyperspectral data using vector quantization

1997 ◽  
Vol 61 (3) ◽  
pp. 419-436 ◽  
Author(s):  
Michael J. Ryan ◽  
John F. Arnold
Keyword(s):  
Vector Quantization ◽  
Lossy Compression ◽  
Hyperspectral Data

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Lossy Compression of Haptic Data by Using DCT

2010 ◽  
Vol 130 (8) ◽  
pp. 945-952 ◽  
Author(s):  
Hiroyuki Tanaka ◽  
Kouhei Ohnishi
Keyword(s):  
Lossy Compression

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images
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