Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/1948
DC Field | Value | Language |
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dc.contributor.authorall | Aiazzi, B.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.authorall | Alparone, L.; Dipartimento di Elettronica e Telecomunicazioni, Università degli Studi di Firenze, Italy | en |
dc.contributor.authorall | Barducci, A.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.authorall | Baronti, S.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.authorall | Marcoionni, P.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.authorall | Pippi, I.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.authorall | Selva, M.; Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.date.accessioned | 2006-12-07T14:35:48Z | en |
dc.date.available | 2006-12-07T14:35:48Z | en |
dc.date.issued | 2006-02 | en |
dc.identifier.uri | http://hdl.handle.net/2122/1948 | en |
dc.description.abstract | The definition of noise models suitable for hyperspectral data is slightly different depending on whether whiskbroom or push-broom are dealt with. Focussing on the latter type (e.g., VIRS-200) the noise is intrinsically non-stationary in the raw digital counts. After calibration, i.e. removing the variability effects due to different gains and offsets of detectors, the noise will exhibit stationary statistics, at least spatially. Hence, separable 3D processes correlated across track (x), along track (y) and in the wavelength (λ), modelled as auto-regressive with GG statistics have been found to be adequate. Estimation of model parameters from the true data is accomplished through robust techniques relying on linear regressions calculated on scatter-plots of local statistics. An original procedure was devised to detect areas within the scatter-plot corresponding to statistically homogeneous pixels. Results on VIRS-200 data show that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and somewhat correlated along and across track by slightly different extents. Spectral correlation has been investigated as well and found to depend both on the sparseness (spectral sampling) and on the wavelength values of the bands that have been selected. | en |
dc.format.extent | 2115698 bytes | en |
dc.format.mimetype | application/pdf | en |
dc.language.iso | English | en |
dc.relation.ispartofseries | 1/49 (2006) | en |
dc.subject | generalised Gaussian probability density function | en |
dc.subject | heavy-tailed distributions | en |
dc.subject | hyperspectral imagery | en |
dc.subject | linear regression | en |
dc.subject | noise modelling | en |
dc.subject | Visible InfraRed Scanner (VIRS-200) | en |
dc.title | Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers | en |
dc.type | article | en |
dc.type.QualityControl | Peer-reviewed | en |
dc.subject.INGV | 04. Solid Earth::04.02. Exploration geophysics::04.02.05. Downhole, radioactivity, remote sensing, and other methods | en |
dc.subject.INGV | 05. General::05.05. Mathematical geophysics::05.05.99. General or miscellaneous | en |
dc.relation.references | AIAZZI, B., L. ALPARONE and S. BARONTI (1999a): Reliably estimating the speckle noise from SAR data, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 1546-1548. AIAZZI, B., L. ALPARONE and S. BARONTI (1999b): Unsupervised assessment and pyramidal filtering of colored speckle, in SAR Image Analysis, Modelling, and Techniques, IV, edited by F. POSA, SPIE Proc., EUROPTO Ser., 3869, 9-20. AIAZZI, B., L. ALPARONE and S. BARONTI (1999c): Estimation based on entropy matching for generalized Gaussian PDF modeling, IEEE Signal Processing Lett., 6, 138-140. AIAZZI, B., L. ALPARONE, A. BARDUCCI, S. BARONTI and I. PIPPI (2001): Information-theoretic assessment of sampled hyperspectral imagers, IEEE Trans. Geosci. Remote Sensing, 39, 1447-1458. AIAZZI, B. L. ALPARONE, A. BARDUCCI, S. BARONTI and I. PIPPI (2002): Estimating noise and information of multispectral imagery, J. Optical Engin., 41, 656-668. BARDUCCI, A. and I. PIPPI (2001): Analysis and rejection of systematic disturbances in hyperspectral remotely sensed images of the Earth, Appl. Optics, 40, 1464-1477. JIMENEZ, L.O. and D.A. LANDGREBE (1998): Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 28, 39-54. JIMENEZ, L.O. and D.A. LANDGREBE (1999): Hyperspectral data analysis and supervised feature reduction with projection pursuit, IEEE Trans. Geosci. Remote Sensing, 37, 2653-2667. LEE, J.-S. and K. HOPPEL (1989): Noise modeling and estimation of remotely sensed images, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 1005-1008. LEE, J.B., A.S. WOODYATT and M. BERMAN (1990): Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform, IEEE Trans. Geosci. Remote Sensing, 28, 295-304. SHARIfi, K. and A. LEON-GARCIA (1995): Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video, IEEE Trans. Circuits Syst. Video Technol., 5 (1), 52-56. | en |
dc.description.journalType | JCR Journal | en |
dc.description.fulltext | open | en |
dc.contributor.author | Aiazzi, B. | en |
dc.contributor.author | Alparone, L. | en |
dc.contributor.author | Barducci, A. | en |
dc.contributor.author | Baronti, S. | en |
dc.contributor.author | Marcoionni, P. | en |
dc.contributor.author | Pippi, I. | en |
dc.contributor.author | Selva, M. | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.department | Dipartimento di Elettronica e Telecomunicazioni, Università degli Studi di Firenze, Italy | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
dc.contributor.department | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | en |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.author.dept | Dipartimento di Elettronica e Telecomunicazioni, Università degli Studi di Firenze, Italy | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.author.dept | Istituto di Fisica Applicata «Nello Carrara» (IFAC), CNR, Firenze, Italy | - |
crisitem.classification.parent | 04. Solid Earth | - |
crisitem.classification.parent | 05. General | - |
Appears in Collections: | Annals of Geophysics |
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01 Aiazzi.pdf | 2.07 MB | Adobe PDF | View/Open |
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