TY - JOUR
T1 - Divide-and-conquer strategies for hyperspectral image processing
T2 - A review of their benefits and advantages
AU - Blanes, Ian
AU - Serra-Sagristà, Joan
AU - Marcellin, Michael W.
AU - Bartrina-Rapesta, Joan
N1 - Funding Information:
We would like to thank Carole Thiebaut for her valuable comments and suggestions. This work has been partially supported by the European Union, the Spanish Government (MICINN), FEDER, and the Catalan Government, under grants FP7-SPACE FP7-242390, FP7-PEOPLE-2009-IIF FP7-250420, TIN2009-14426-C02-01, FPU AP2007-01555, and 2009-SGR-1224. The computational resources used in this article were partially provided by the Oliba Project of the Universitat Autònoma de Barcelona.
PY - 2012/5
Y1 - 2012/5
N2 - In the field of geophysics, huge volumes of information often need to be processed with complex and time-consuming algorithms to better understand the nature of the data at hand. A particularly useful instrument within a geophysicists toolbox is a set of decorrelating transforms. Such transforms play a key role in the acquisition and processing of satellite-gathered information, and notably in the processing of hyperspectral images. Satellite images have a substantial amount of redundancy that not only renders the true nature of certain events less perceivable to geophysicists but also poses an issue to satellite makers, who have to exploit this data redundancy in the design of compression algorithms due to the constraints of down-link channels. This issue is magnified for hyperspectral imaging sensors, which capture hundreds of visual representations of a given targeteach representation (called a component or a band) for a small range of the light spectrum. Although seldom alone, decorrelation transforms are often used to alleviate this situation by changing the original data space into a representation where redundancy is decreased and valuable information is more apparent.
AB - In the field of geophysics, huge volumes of information often need to be processed with complex and time-consuming algorithms to better understand the nature of the data at hand. A particularly useful instrument within a geophysicists toolbox is a set of decorrelating transforms. Such transforms play a key role in the acquisition and processing of satellite-gathered information, and notably in the processing of hyperspectral images. Satellite images have a substantial amount of redundancy that not only renders the true nature of certain events less perceivable to geophysicists but also poses an issue to satellite makers, who have to exploit this data redundancy in the design of compression algorithms due to the constraints of down-link channels. This issue is magnified for hyperspectral imaging sensors, which capture hundreds of visual representations of a given targeteach representation (called a component or a band) for a small range of the light spectrum. Although seldom alone, decorrelation transforms are often used to alleviate this situation by changing the original data space into a representation where redundancy is decreased and valuable information is more apparent.
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U2 - 10.1109/MSP.2011.2179416
DO - 10.1109/MSP.2011.2179416
M3 - Review article
AN - SCOPUS:85032751656
SN - 1053-5888
VL - 29
SP - 71
EP - 81
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 3
M1 - 6179815
ER -