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  • lorsque la demande peut être examinée par le public;
  • lorsque le brevet est émis (délivrance).
(12) Brevet: (11) CA 2253145
(54) Titre français: SYSTEME DE VISUALISATION ET D'ANALYSE INTERACTIVES D'ENSEMBLES DE DONNEES DE RADIOMETRIE SPECTRALE IMAGEANTE AU MOYEN D'UN RESEAU A GRANDE DISTANCE
(54) Titre anglais: SYSTEM FOR INTERACTIVE VISUALIZATION AND ANALYSIS OF IMAGING SPECTROMETRY DATASETS OVER A WIDE-AREA NETWORK
(51) Classification internationale des brevets (CIB):
  • H04N 19/94 (2014.01)
  • G01J 3/02 (2006.01)
(72) Inventeurs (Pays):
  • HOLLINGER, ALLAN B. (Canada)
  • QIAN, SHEN-EN (Canada)
  • WILLIAMS, DANIEL J. (Canada)
(73) Titulaires (Pays):
  • CANADIAN SPACE AGENCY (Canada)
(71) Demandeurs (Pays):
  • CANADIAN SPACE AGENCY (Canada)
(74) Agent: MACRAE & CO.
(45) Délivré: 2006-12-12
(22) Date de dépôt: 1998-10-30
(41) Mise à la disponibilité du public: 1999-04-30
Requête d’examen: 2003-07-16
(30) Licence disponible: S.O.
(30) Langue des documents déposés: Anglais

(30) Données de priorité de la demande:
Numéro de la demande Pays Date
2,219,809 Canada 1997-10-31
60/063,796 Etats-Unis d'Amérique 1997-10-31

Abrégé français

La présente invention se rapporte à une méthode qui permet d'afficher et traiter des données d'images hyperspectrales comprimées à l'aide d'un algorithme de quantification vectorielle. Selon l'invention, les données sont comprimées à l'aide d'une table de codes de vecteurs de code comprenant des vecteurs spectraux binaires, permettant ainsi de traiter les données comprimées et de les afficher dans un cube de données sans que celles-ci se déploient sur l'ensemble du cube de données. Afin d'afficher une image provenant d'un cube de données, un emplacement est sélectionné au sein de ce dernier pour chaque pixel de l'image, une valeur d'indice est ensuite extraite d'une carte-index à cet emplacement , puis une valeur spectrale est extraite d'un vecteur spectral de la table de codes pour ledit pixel, le vecteur spectral étant ainsi identifié par l'indice extrait. Un vecteur spectral unique est aisément visible par l'affichage d'un vecteur spectral provenant d'une table de codes.


Abrégé anglais

The present invention relates to a method of viewing and processing hyper- spectral image data compressed using a VQ algorithm. According to the invention the data is compressed using a codebook of codevectors including binary spectral vectors, which allows processing of the compressed data and viewing of data within a datacube without expanding the compressed data into the complete datacube. In order to view an image derived from a datacube, for each pixel within the image a location within the datacube is selected, an index value from the index map at that location is retrieved, and a spectral value from a spectral vector within the codebook is retrieved for said pixel, the spectral vector identified by the retrieved index. A single spectral vector is easily viewed by displaying a spectral vector from the codebook.


Note : Les revendications sont présentées dans la langue officielle dans laquelle elles ont été soumises.


What is claimed is:

1. A method of processing hyper-spectral image data defining signal levels of
multiple spectral
bands in a compressed form, the compressed form comprising an array of stored
indices and a
codebook comprising spectral vectors each comprising a plurality of spectral
values each
relating to one and only one predetermined spectral band of a plurality of
spectral bands, the
method comprising the steps of:
for each image pixel,
determining a first band from the multiple spectral bands and an index from
the array of
stored indices, the index indicative of a vector having values each relating
to one and
only one predetermined spectral band for each of the plurality of the multiple
spectral
bands within the codebook of spectral vectors, and
retrieving from the indicated spectral vector within the codebook of spectral
vectors a
value corresponding to the first band.
2. A method according to claim 1 comprising the step of displaying an image
comprising image
pixels, image pixel values based upon the values retrieved for each image
pixel.
3. A method according to claim 1 comprising the step of storing the image
comprising image
pixels, image pixel values based upon the values retrieved for each image
pixel.
4. A method of processing hyper-spectral image data as defined in claim 1
wherein each
spectral vector comprises data from a plurality of spectral bands, each band
representative of a
range of wavelengths .lambda..
5. A method of processing hyper-spectral image data as defined in claim 1
wherein each
spectral vector comprises data from a plurality of bands, each band
representative of one of a
time and a range of times.



33


6. A method of processing hyper-spectral image data as defined in claim 1
wherein the step of
retrieving from the indicated vector within the codebook of spectral vectors a
value is
performed by retrieving one and only one value, the value corresponding to the
first band and
the index.
7. A method of processing hyper-spectral image data as defined in claim 1
comprising the steps
of:
for each image pixel,
determining a second band from the multiple spectral bands and an index from
the array
of stored indices, the index indicative of a vector having values for each of
the plurality
of the multiple spectral bands within the codebook of spectral vectors, and
retrieving from the indicated vector within the codebook of spectral vectors a
value
corresponding to the second band.
8. A method of processing hyper-spectral image data as defined in claim 7
comprising the step
of displaying a plurality of values corresponding to the first band and the
second band, a value
corresponding to the first band displayed in a first colour and a value
corresponding to the
second band displayed in a second other colour.
9. A method of processing hyper-spectral image data as defined in claim 8
wherein the steps of
retrieving a value are performed over a public network, the values retrieved
from network
accessible data.
10. A method of processing hyper-spectral image data as defined in claim 1
comprising the
steps of:
providing decompressed hyper-spectral data;
for each image pixel, retrieving from the decompressed hyper-spectral data a
value
corresponding to first band; and,
displaying the values corresponding to the first band and retrieved from the
codebook of



34


spectral vectors in a first colour and displaying the values corresponding to
the first band and
retrieved from the decompressed hyper-spectral data in a second other colour.
11. A method of processing hyper-spectral image data defining signal levels of
multiple spectral
bands per image pixel in a compressed form, the compressed form comprising an
array of
stored indices and a codebook of spectral vectors, the method comprising the
step of:
processing spectral vectors having values for each of a plurality of the
multiple spectral bands
within the codebook of spectral vectors to produce a processed codebook, each
spectral vector
processed in isolation.
12. A method of processing hyper-spectral image data as defined in claim 11
wherein during
the step of processing spectral vectors one or more spectral bands within each
spectral vector
within the codebook of spectral vectors is processed.
13. A method of processing hyper-spectral image data as defined in claim 11
wherein during
the step of processing spectral vectors, the spectral vectors are sub-sampled
to provide second
spectral vectors having less data than the spectral vectors.
14. A method of processing hyper-spectral image data as defined in claim 11
wherein the step
of processing comprises at least one of highlighting, selecting,
mathematically filtering,
mathematically altering and performing unsupervised classification.
15. A method of processing hyper-spectral image data as defined in claim 11
wherein the step
of processing comprises applying logical rules to the hyper-spectral image
data.
16. A method of processing hyper-spectral image data as defined in claim 12
comprising the
steps of:
for each image pixel, determining a band from the multiple spectral bands and
an index from
the array of stored indices, the index indicative of a vector within the
processed codebook and



35


retrieving from the indicated vector within the processed codebook a single
value
corresponding to the determined band; and,
displaying an image comprising pixels, pixel values corresponding with the
values retrieved for
each pixel.
17. A method of processing hyper-spectral image data as defined in claim 16
wherein the step
of processing comprises reducing the information content of each spectral
vector within the
codebook of spectral vectors, the method comprising the steps of:
transmitting the processed codebook via a communication medium from a first
computer to a
second other computer;
receiving the processed codebook at the second other computer,
wherein the step of displaying is performed at the second other computer.
18. A method of processing hyper-spectral image data as defined in claim 11
wherein the step
of processing includes the steps of:
providing searching criteria, each of which is determinable within a single
same spectral band;
and,
searching by applying the searching criteria to each vector within the
codebook of spectral
vectors to determine spectral vectors, portions of which meet the search
criteria, and to store
data relating to the determined spectral vectors.
19. A method of processing hyper-spectral image data as defined in claim 18
wherein the data
relating to the determined spectral vectors includes an indication of the
determined spectral
vectors.
20. A method of processing hyper-spectral image data as defined in claim 18
wherein the data
relating to the determined spectral vectors comprises transformed spectral
vectors.
21. A method of processing hyper-spectral image data as defined in claim 18
wherein the step



36


of processing comprises reducing the information content of each spectral
vector within the
codebook of spectral vectors, the method comprising the steps of:
transmitting the processed codebook via a communication medium from a first
computer to a
second other computer;
receiving the processed codebook at the second other computer.
22. A method of processing hyper-spectral image data as defined in claim 21
comprising the
steps of:
for each pixel, determining a band from the multiple spectral bands and an
index from the array
of stored indices, the index indicative of a spectral vector having values for
each of a plurality
of the multiple spectral bands within the codebook of spectral vectors and
retrieving from the
indicated spectral vector within the codebook of spectral vectors a value
corresponding to the
determined band;
retrieving the stored indication; and,
displaying on the second other computer an image comprising image pixels,
image pixel values
based on the values retrieved for each image pixel and the retrieved
indications.
23. A method of processing hyper-spectral image data as defined in claim 22
wherein the
displayed image includes highlighted pixels, highlighting determined based on
the stored
indications.
24. A method of processing hyper-spectral image data as defined in claim 11
comprising the
steps of:
transmitting the codebook of spectral vectors and the array of stored indices
from a first
computer to a second other computer, the second other computer remote from the
first
computer,
wherein the step of processing is performed on the second other computer and
includes
determining from the codebook of spectral vectors and the array of stored
indices, data within
the hyper-spectral data that is significant for further analysis;



37


requesting the data from the first computer;
compressing decompressed hyper-spectral data associated with the requested
data according to
a known compression algorithm to produce compressed data; and,
transmitting the compressed data to the second computer.
25. A method of processing hyper-spectral image data as defined in claim 24
wherein the
compressed data has a higher fidelity than the codebook of spectral vectors
and the array of
stored indices.
26. A method of processing hyper-spectral image data as defined in claim 25
wherein the step
of compressing consists of performing lossless compression.
27. A method of processing hyper-spectral image data as defined in claim 11
comprising the
step of retrieving information indicative of spectral vector significance, the
spectral vector
significance determined using significance analysis.
28. A method of processing hyper-spectral image data as defined in claim 27
wherein the
information is retrieved from within at least one of the codebook of spectral
vectors and the
stored indices.
29. A method of processing hyper-spectral image data as defined in claim 28
wherein the
information includes at least one of mean, average, and standard deviation.
30. A method of processing hyper-spectral image data as defined in claim 28
wherein the
information includes an ordering of the hyper-spectral image data.
31. A method of processing hyper-spectral image data as defined in claim 28
wherein the
information includes at least one of a histogram, a running average, principle
component
analysis, unsupervised classification, spectral mixing, and filtering.



38


32. A method of processing hyper-spectral image data defining signal levels of
multiple spectral
bands per image pixel in a compressed form, the compressed form comprising an
array of
stored indices and a codebook of spectral vectors, the method comprising the
step of:
processing spectral vectors having values each relating to one and only one
predetermined
spectral band for each of a plurality of the multiple spectral bands within
the codebook of
spectral vectors and data from the index map to extract information from the
codebook of
spectral vectors and index map other than pixel values for display absent a
step of extracting a
decompressed datacube from the codebook of spectral vectors and index map.
33. A method of processing hyper-spectral image data as defined in claim 32
wherein the
information is indicative of spectral vector significance, the spectral vector
significance
determined using significance analysis.
34. A method of processing hyper-spectral image data as defined in claim 33
wherein
significance is determined by searching for predetermined statistical
information within the
codebook of spectral vectors and stored indices.
35. A method of processing hyper-spectral image data as defined in claim 34
wherein the
statistical information includes at least one of mean, average, and standard
deviation.
36. A method of processing hyper-spectral image data as defined in claim 34
wherein the
statistical information includes an ordering of the hyper-spectral image data.
37. A method of processing hyper-spectral image data as defined in claim 34
wherein the
statistical information includes at least one of a histogram, a running
average, principle
component analysis, unsupervised classification, spectral mixing, and
filtering.
38. A method of processing hyper-spectral image data as defined in claim 32
wherein the
information is another index map.



39



39. A system for processing hyper-spectral image data defining signal levels
of multiple
spectral bands in a compressed form, the compressed form comprising an array
of stored
indices and a codebook comprising spectral vectors each comprising a plurality
of spectral
vectors, the system comprising:
means for selecting for each image pixel a first band from the multiple
spectral bands and an
index from the array of stored indices, the index indicative of a spectral
vector having values
each relating to one and only one predetermined spectral band for each of the
plurality of the
multiple spectral bands within the codebook of spectral vectors, and
means for retrieving for each image pixel from the indicated vector within the
codebook of
spectral vectors a value corresponding to the first band; and,
means for displaying an image comprising image pixels, image pixel values
based upon the
values retrieved for each image pixel.
40. A system as defined in claim 39 wherein the means for retrieving a value
includes
communication means for communicating via a public network.
41. A system as defined in claim 39 wherein the means for displaying an image
includes means
for displaying a plurality of values corresponding to the first band in a
first colour and other
values in a second other colour.
42. A method of processing hyper-spectral image data defining signal levels of
multiple spectral
bands in a compressed form, the compressed form comprising an array of stored
indices and a
codebook comprising spectral vectors each comprising a plurality of spectral
vectors, the
method comprising the steps of:
for each image pixel,
determining a first band from the multiple spectral bands and an index from
the array of stored
indices, the index indicative of a spectral vector having values for each of
the plurality of the
multiple spectral bands within the codebook of spectral vectors, and
retrieving from the indicated vector having values for each of the plurality
of the multiple
40


spectral bands within the codebook of spectral vectors a value corresponding
to the first band;
and,
displaying an image comprising image pixels, image pixel values based upon the
values
retrieved for each image pixel.
43. A method of processing hyper-spectral image data defining signal levels of
multiple spectral
bands in a compressed form, the compressed form comprising an array of stored
indices and a
codebook comprising a plurality of vectors, the method comprising the steps
of:
for each image pixel,
determining a first band from the multiple spectral bands and an index from
the array of
indices, the index indicative of a vector having values each relating to one
and only one
predetermined spectral band for each of the plurality of the multiple spectral
bands within the
codebook, and
retrieving from the indicated vector within the codebook of spectral vectors a
value
corresponding to the first band.
44. A method of processing hyper-spectral image data as defined in claim 16
wherein the step
of processing comprises the steps of modifying the information content of the
codebook of
spectral vectors, the method comprising the steps of:
retrieving the codebook from a computer;
processing the codebook at a computer, wherein the step of displaying is
performed at a same
computer as the computer on which the data is processed.
41


Une figure unique qui représente un dessin illustrant l’invention.

Pour une meilleure compréhension de l’état de la demande ou brevet qui figure sur cette page, la rubrique Mise en garde , et les descriptions de Brevet , États administratifs , Taxes périodiques et Historique des paiements devraient être consultées.

États admin

Titre Date
(22) Dépôt 1998-10-30
(41) Mise à la disponibilité du public 1999-04-30
Requête d'examen 2003-07-16
(45) Délivré 2006-12-12
Périmé 2014-10-30

Historique des paiements

Type de taxes Anniversaire Échéance Montant payé Date payée
Dépôt 300,00 $ 1998-10-30
Taxe périodique - Demande - nouvelle loi 2 2000-10-30 100,00 $ 2000-09-05
Taxe périodique - Demande - nouvelle loi 3 2001-10-30 100,00 $ 2001-09-19
Taxe périodique - Demande - nouvelle loi 4 2002-10-30 100,00 $ 2002-08-15
Requête d'examen 400,00 $ 2003-07-16
Taxe périodique - Demande - nouvelle loi 5 2003-10-30 150,00 $ 2003-09-03
Taxe périodique - Demande - nouvelle loi 6 2004-11-01 200,00 $ 2004-09-22
Taxe périodique - Demande - nouvelle loi 7 2005-10-31 200,00 $ 2005-10-13
Final 300,00 $ 2006-08-30
Taxe périodique - Demande - nouvelle loi 8 2006-10-30 200,00 $ 2006-09-18
Taxe périodique - brevet - nouvelle loi 9 2007-10-30 200,00 $ 2007-09-25
Taxe périodique - brevet - nouvelle loi 10 2008-10-30 250,00 $ 2008-10-15
Taxe périodique - brevet - nouvelle loi 11 2009-10-30 250,00 $ 2009-10-27
Taxe périodique - brevet - nouvelle loi 12 2010-11-01 250,00 $ 2010-09-21
Taxe périodique - brevet - nouvelle loi 13 2011-10-31 250,00 $ 2011-10-07
Taxe périodique - brevet - nouvelle loi 14 2012-10-30 250,00 $ 2012-10-16

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Filtre Télécharger sélection en format PDF (archive Zip)
Description du
Document
Date
(yyyy-mm-dd)
Nombre de pages Taille de l’image (Ko)
Abrégé 1998-10-30 1 22
Revendications 2006-01-16 9 371
Revendications 1998-10-30 8 317
Page couverture 2006-11-15 2 48
Page couverture 1999-05-25 2 69
Description 2006-01-16 32 1 463
Description 1998-10-30 32 1 466
Dessins 2006-01-16 5 117
Dessins 1998-10-30 5 108
Poursuite-Amendment 2006-01-16 21 908
Poursuite-Amendment 2005-07-28 4 148
Poursuite-Amendment 2003-07-16 1 26
Dessins représentatifs 2006-12-11 1 10
Dessins représentatifs 2006-11-15 1 10
Dessins représentatifs 1999-05-25 1 9