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US6018587A
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Method for remote sensing analysis be decorrelation statistical analysis and hardware therefor
Assignee: Applied Spectral Imaging Ltd
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Filing: 1997-07-01
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Granted: 2000-01-25
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Expires: 2017-07-01
Abstract
Abstract A method for remote scenes classification comprising the steps of (a) preparing a reference template for classification of the remote scenes via (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of the preclassified reference scenes; (iii) employing a principal component analysis for extracting the spectral cube for decorrelated spectral data characterizing the reference scenes; and (vi) using at least a part of the decorrelated spectral data for the preparation of the reference template for remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in the remote scenes is obtained; (c) employing a decorrelation statistical method for extracting decorrelated spectral data characterizing the pixels; and (d) comparing at least a part of the decorrelated spectral data extracted from the pixels of the remote scenes with the reference template.
Inventors
- 👤 Dario Cabib
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-07 09:33
CPC Classifications
G; G01; G01J; G01J3/00; G01J3/12; G01J3/1256; G01J3/26; G01J3/28; G01J3/2823; G01J3/44; G01J3/4406; G01J3/45; G01J3/453; G01N; G01N21/00; G01N21/62; G01N21/63; G01N21/64; G01N21/6428; G01N21/645; G01N21/6456; G01N21/6458; G01N33/00; G01N33/48; G01N33/50; G01N33/5005; G01N33/53; G01N33/569; G01N33/56966; G01N33/58; G01N33/582; G06; G06V; G06V10/00; G06V10/88; G06V20/00; G06V20/10; G06V20/13; G06V20/60; G06V20/64; G06V20/69; G01J2003/2866; G01J3/02
Claims Analysis
Total Claims: 48
Claim 1
Independent Claim
92 words
A method for preparing a reference template for classification of remote scenes comprising the steps of: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified scenes; (b) using a spectral imager for measuring a spectral cube of said preclassified referenced scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for remote scenes classification.
Claim 2
Independent Claim
92 words
A method for preparing a reference template for classification of remote scenes comprising the steps of: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified scenes; (b) using a spectral imager for measuring a spectral cube of said preclassified referenced scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for remote scenes classification.
Claim 3
Dependent Claim
40 words
The method for preparing a reference template for classification of remote scenes as in claim 1, wherein said spectral imager includes an element selected from the group consisting of a dispersion element, a filter, a tunable filter and an interferometer.
Claim 4
Dependent Claim
38 words
The method for preparing a reference template for classification of remote scenes as in claim 1, wherein said decorrelation statistical method is selected from the group consisting of principal component analysis, canonical variable analysis and singular value decomposition.
Claim 5
Dependent Claim
33 words
The method for preparing a reference template for classification of remote scenes as in claim 3, wherein said principal component analysis includes expressing each of said scenes as linear combinations of N eigenvectors.
Claim 6
Dependent Claim
24 words
The method for preparing a reference template for classification of remote scenes as in claim 4, wherein N is an integer greater than two.
Claim 7
Dependent Claim
28 words
The method for preparing a reference template for classification of remote scenes as in claim 4, wherein N is an integer greater than two and smaller than eight.
Claim 8
Dependent Claim
140 words
The method for preparing a reference template for classification of remote scenes as in claim 3, wherein said principal component analysis includes the steps of: (a) selecting k spectral slices for said spectral cube of said reference scenes; (b) calculating an average spectrum for each of said reference scenes; (c) stretching each of said average spectra for obtaining a stretched average spectrum for each of said reference scenes; (d) averaging said stretched average spectra for each of said reference scenes, for obtaining an ensemble average spectrum for each of said reference scenes; (e) calculating a k dimension eigen system for said ensemble average spectra and extracting N eigenvectors; (f) using said N eigenvectors for defining an N-dimension vector for each of said reference scenes; and (g) using said N-dimension vectors for preparing the reference template for the remote scenes classification.
Claim 9
Dependent Claim
24 words
The method for preparing a reference template for classification of remote scenes as in claim 7, wherein k is an integer greater than nine.
Claim 10
Dependent Claim
24 words
The method for preparing a reference template for classification of remote scenes as in claim 7, wherein N is an integer greater than two.
Claim 11
Dependent Claim
36 words
The method for preparing a reference template for classification of remote scenes as in claim 7, wherein said principal component analysis further includes the step of: (h) performing a spatial averaging procedure over all spectral slices.
Claim 12
Independent Claim
166 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of the remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a principal component analysis for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (vi) using at least a part of said decorrelated spectral data for the preparation of the reference template for remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in said remote scenes is obtained; (c) employing a decorrelation statistical method for extracting decorrelated spectral data characterizing said pixels; and (d) comparing at least a part of said decorrelated spectral data extracted from said pixels of the remote scenes with said reference template.
Claim 13
Independent Claim
166 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of the remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a principal component analysis for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (vi) using at least a part of said decorrelated spectral data for the preparation of the reference template for remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in said remote scenes is obtained; (c) employing a decorrelation statistical method for extracting decorrelated spectral data characterizing said pixels; and (d) comparing at least a part of said decorrelated spectral data extracted from said pixels of the remote scenes with said reference template.
Claim 14
Independent Claim
162 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassifled reference scenes; (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (iv) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in said remote scenes is obtained; (c) projecting said spectrum of each of said pixels onto said decorrelated spectral data for obtaining a projected spectrum for each of said pixels; and (d) comparing said projected spectra with said reference template.
Claim 15
Independent Claim
162 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassifled reference scenes; (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (iv) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in said remote scenes is obtained; (c) projecting said spectrum of each of said pixels onto said decorrelated spectral data for obtaining a projected spectrum for each of said pixels; and (d) comparing said projected spectra with said reference template.
Claim 16
Dependent Claim
26 words
The method for remote scenes classification as in claim 11, further comprising the step of: (f) according to said comparison, attributing each pixel an artificial color.
Claim 17
Dependent Claim
31 words
The method for remote scenes classification as in claim 11, wherein said spectral imager includes an element selected from the group consisting of a dispersion element, a filter and an interfcrometer.
Claim 18
Dependent Claim
32 words
The method for remote scenes classification as in claim 11, wherein said decorrelation statistical method is selected from the group consisting of principal component analysis, canonical variable analysis and singular value decomposition.
Claim 19
Dependent Claim
28 words
The method for remote scenes classification as in claim 15, wherein said principal component analysis includes expressing each of said analyzed scenes as linear combinations of N eigenvectors.
Claim 20
Independent Claim
257 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a principal component analysis for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes via: (a) selecting k spectral slices for said spectral cube of said reference scenes; (b) calculating an average spectrum for each of said reference scenes; (c) stretching said average spectra for obtaining a stretched average spectrum for each of said reference scenes; (d) averaging said stretched average spectra for each of said reference scenes for obtaining an ensemble average spectrum for each of said reference scenes; (e) calculating a k dimension eigen system for said ensemble average spectra and extracting N eigenvectors; (f) using said N eigenvectors for defining an N-dimension vector for each of said reference scenes; and (g) using said N-dimension vectors for preparing said reference template for the remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of the remote scenes, such that a spectrum of each pixel in the remote scenes is obtained; (c) projecting said spectrum of each of said pixels into said N eigenvectors for obtaining a projected N dimension vector for each of said pixels; and (d) correlating each of said projected N dimension vectors with said reference template.
Claim 21
Independent Claim
257 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a principal component analysis for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes via: (a) selecting k spectral slices for said spectral cube of said reference scenes; (b) calculating an average spectrum for each of said reference scenes; (c) stretching said average spectra for obtaining a stretched average spectrum for each of said reference scenes; (d) averaging said stretched average spectra for each of said reference scenes for obtaining an ensemble average spectrum for each of said reference scenes; (e) calculating a k dimension eigen system for said ensemble average spectra and extracting N eigenvectors; (f) using said N eigenvectors for defining an N-dimension vector for each of said reference scenes; and (g) using said N-dimension vectors for preparing said reference template for the remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of the remote scenes, such that a spectrum of each pixel in the remote scenes is obtained; (c) projecting said spectrum of each of said pixels into said N eigenvectors for obtaining a projected N dimension vector for each of said pixels; and (d) correlating each of said projected N dimension vectors with said reference template.
Claim 22
Dependent Claim
26 words
The method for remote scenes classification as in claim 17, the method further comprising the step of performing a spatial averaging procedure on all spectral slices.
Claim 23
Independent Claim
122 words
A method of calculating decorrelation matched filters for remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the method comprising the step of: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining a mathematical description of the decorrelation matched filters.
Claim 24
Independent Claim
122 words
A method of calculating decorrelation matched filters for remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the method comprising the step of: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining a mathematical description of the decorrelation matched filters.
Claim 25
Dependent Claim
42 words
The method of calculating decorrelation matched filters for remote scenes classification as in claim 19, wherein said decorrelated spectral data is obtained using a principal component analysis, which includes expressing each of said reference scenes by a linear combination of N eigenvectors.
Claim 26
Dependent Claim
134 words
A set of decorrelation matched filters for remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the set comprising physical filters having shapes, said shapes following a mathematical description, said mathematical description being obtainable by: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of the decorrelation matched filters.
Claim 27
Dependent Claim
134 words
A set of decorrelation matched filters for remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the set comprising physical filters having shapes, said shapes following a mathematical description, said mathematical description being obtainable by: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of the decorrelation matched filters.
Claim 28
Independent Claim
146 words
A method of tuning a tunable filter for remote scenes classification, the method renders the tunable filter to mimic a set of decorrelation matched filters, and is for extracting decorrelated spectral data from the remote scenes, the method comprising the steps of: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube ol said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining a mathematical description describing the set of decorrelation matched filters; and (c) sequentially tuning the tunable filter according to said mathematical description.
Claim 29
Independent Claim
146 words
A method of tuning a tunable filter for remote scenes classification, the method renders the tunable filter to mimic a set of decorrelation matched filters, and is for extracting decorrelated spectral data from the remote scenes, the method comprising the steps of: (a) obtaining decorrelated spectral data characterizing a set of reference scenes via: (i) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube ol said preclassified reference scenes; and (iii) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; (b) mathematically manipulating at least a part of said decorrelated spectral data for obtaining a mathematical description describing the set of decorrelation matched filters; and (c) sequentially tuning the tunable filter according to said mathematical description.
Claim 30
Dependent Claim
29 words
The method of tuning a tunable filter for remote scenes classification as in claim 22, wherein said tunable filter is selected from the group consisting of AOTF and LCTF.
Claim 31
Independent Claim
188 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of the remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a decorrelation statistical method lor extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (iv) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (b) calculating a mathematical description of decorrelation matched filters for classification of the remote scenes employing said reference template, said calculation being by mathematically manipulating at least a part of said decorrelated spectral data; (c) using said mathematical description of said decorrelation matched filters for manufacturing said decorrelation matched filters; (d) using said decorrelation matched filters for extracting decorrelated spectral data from each pixel of the remote scenes; and (e) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 32
Independent Claim
188 words
A method for remote scenes classification comprising the steps of: (a) preparing a reference template for classification of the remote scenes via: (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (iii) employing a decorrelation statistical method lor extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (iv) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (b) calculating a mathematical description of decorrelation matched filters for classification of the remote scenes employing said reference template, said calculation being by mathematically manipulating at least a part of said decorrelated spectral data; (c) using said mathematical description of said decorrelation matched filters for manufacturing said decorrelation matched filters; (d) using said decorrelation matched filters for extracting decorrelated spectral data from each pixel of the remote scenes; and (e) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 33
Dependent Claim
26 words
The method for remote scenes classification as in claim 24, further comprising the step of: (f) attributing each pixel an artificial color according to said comparison.
Claim 34
Dependent Claim
32 words
The method for remote scenes classification as in claim 24, wherein said decorrelation statistical method is selected from the group consisting of principal component analysis, canonical variable analysis and singular value decomposition.
Claim 35
Dependent Claim
28 words
The method for remote scenes classification as in claim 26, wherein said principal component analysis includes expressing each of said reference scenes as linear combinations of N eigenvectors.
Claim 36
Independent Claim
206 words
A method for remote scenes classification comprising the steps of: (a) providing a set of decorrelation matched filters for the remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the set including physical filters having shapes, said shapes following a mathematical description, said mathematical description being achieved by: (i) preparing a reference template for classification of remote scenes via: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of said decorrelation matched filters; (b) using said decorrelation matched filters for extracting decorrelated spectral data from each pixel of the remote scenes; and (c) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 37
Independent Claim
206 words
A method for remote scenes classification comprising the steps of: (a) providing a set of decorrelation matched filters for the remote scenes classification, the decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, the set including physical filters having shapes, said shapes following a mathematical description, said mathematical description being achieved by: (i) preparing a reference template for classification of remote scenes via: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of said decorrelation matched filters; (b) using said decorrelation matched filters for extracting decorrelated spectral data from each pixel of the remote scenes; and (c) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 38
Dependent Claim
26 words
The method for remote scenes classification as in claim 28, further comprising the step of: (d) according to said comparison, attributing each pixel an artificial color.
Claim 39
Dependent Claim
32 words
The method for remote scenes classification as in claim 28, wherein said decorrelation statistical method is selected from the group consisting of principal component analysis, canonical variable analysis and singular value decomposition.
Claim 40
Dependent Claim
28 words
The method for remote scenes classification as in claim 30, wherein said principal component analysis includes expressing each of said reference scenes as linear combinations of N eigenvectors.
Claim 41
Independent Claim
213 words
A method for remote scenes classification comprising the steps of: (a) providing a tunable filter and tuning information for tuning said tunable filter so as to mimic a set of decorrelation matched filters, said tunable filter being for extracting decorrelated spectral data from the remote scenes, said tuning information being achieved by: (i) preparing a reference template for classification of remote scenes via: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassificd reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said tuning information, which is a mathematical description describing said set of decorrelation matched filters; and (b) using said information for sequentially tuning said tunable filter for extracting decorrelated spectral data from each pixel of the remote scenes; and (c) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 42
Independent Claim
213 words
A method for remote scenes classification comprising the steps of: (a) providing a tunable filter and tuning information for tuning said tunable filter so as to mimic a set of decorrelation matched filters, said tunable filter being for extracting decorrelated spectral data from the remote scenes, said tuning information being achieved by: (i) preparing a reference template for classification of remote scenes via: (a) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassificd reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; (c) employing a decorrelation statistical method for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (d) using at least a part of said decorrelated spectral data for the preparation of the reference template for the remote scenes classification; (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said tuning information, which is a mathematical description describing said set of decorrelation matched filters; and (b) using said information for sequentially tuning said tunable filter for extracting decorrelated spectral data from each pixel of the remote scenes; and (c) comparing said decorrelated spectral data extracted from each pixel of the remote scenes with the reference template.
Claim 43
Dependent Claim
192 words
A spectral decorrelation measurement apparatus for remote scenes classification by extracting decorrelated spectral data from the remote scenes, the apparatus is connected to a telescope used to view the remote scenes, said apparatus comprising: (a) a detector; and (b) an optical system for transmitting electromagnetic radiation from the remote scenes onto said detector, said optical system including a set of decorrelating matched filters, said decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, said filters of said set of decorrelatio n matched filters having shapes, said shapes following a mathematical description, said mathematical description being calculated by: (i) obtaining decorrelated spectral data characterizing a set of reference scenes via: (a) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (c) employing a decorrelation statistical method flor extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of the decorrelation matched filters.
Claim 44
Dependent Claim
192 words
A spectral decorrelation measurement apparatus for remote scenes classification by extracting decorrelated spectral data from the remote scenes, the apparatus is connected to a telescope used to view the remote scenes, said apparatus comprising: (a) a detector; and (b) an optical system for transmitting electromagnetic radiation from the remote scenes onto said detector, said optical system including a set of decorrelating matched filters, said decorrelation matched filters being for extracting decorrelated spectral data from the remote scenes, said filters of said set of decorrelatio n matched filters having shapes, said shapes following a mathematical description, said mathematical description being calculated by: (i) obtaining decorrelated spectral data characterizing a set of reference scenes via: (a) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes; and (c) employing a decorrelation statistical method flor extracting said spectral cube for decorrelated spectral data characterizing said reference scenes; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of the decorrelation matched filters.
Claim 45
Dependent Claim
32 words
The spectral decorrelation measurement apparatus for remote scenes classification as in claim 33, wherein said optical system further includes a collimating lens for collimating radiation reaching any of said decorrelating matched filters.
Claim 46
Dependent Claim
26 words
The spectral decorrelation measurement apparatus for remote scenes classification as in claim 33, wherein said decorrelation matched filters are arranged on a rotatable filter carrying element.
Claim 47
Dependent Claim
220 words
A spectral decorrelation measurement apparatus for remote scenes classification by extracting decorrelated spectral data from the remote scenes, the apparatus is connected to a telescope used to view the remote scenes, said apparatus comprising: (a) a detector; and (b) an optical system for transmitting electromagnetic radiation from the remote scenes onto said detector, said optical system including a tunable filter and a tuning device, said tuning device being for tuning said tunable filter, so that said tunable filter sequentially mimics a set of decorrelating matched filters, said decorrelation matched filters mimicked by said tunable filter being for extracting decorrelated spectral data from the remote scenes, said tuning of said tunable filter being calculated according to a mathematical description, said mathematical description being calculated by: (i) obtaining decorrelated spectral data characterizing a set of reference scenes via: (a) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassificd reference scenes; and (c) employing a decorrelation statistical method for extracting said spectral cube for said spectral cube for decorrelated spectral data characterizing said reference scenes; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of said mimicked decorrelation matched filters.
Claim 48
Dependent Claim
220 words
A spectral decorrelation measurement apparatus for remote scenes classification by extracting decorrelated spectral data from the remote scenes, the apparatus is connected to a telescope used to view the remote scenes, said apparatus comprising: (a) a detector; and (b) an optical system for transmitting electromagnetic radiation from the remote scenes onto said detector, said optical system including a tunable filter and a tuning device, said tuning device being for tuning said tunable filter, so that said tunable filter sequentially mimics a set of decorrelating matched filters, said decorrelation matched filters mimicked by said tunable filter being for extracting decorrelated spectral data from the remote scenes, said tuning of said tunable filter being calculated according to a mathematical description, said mathematical description being calculated by: (i) obtaining decorrelated spectral data characterizing a set of reference scenes via: (a) classifying said set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (b) using a first spectral imager for measuring a spectral cube of said preclassificd reference scenes; and (c) employing a decorrelation statistical method for extracting said spectral cube for said spectral cube for decorrelated spectral data characterizing said reference scenes; and (ii) mathematically manipulating at least a part of said decorrelated spectral data for obtaining said mathematical description of said mimicked decorrelation matched filters.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2003-01-24 | 2003-07-26 | 2004-01-25 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2007-01-24 | 2007-07-26 | 2008-01-25 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2011-01-24 | 2011-07-26 | 2012-01-25 | $7700.00 | Window Closed / Lapsed |