CN102346129A - Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change - Google Patents
Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change Download PDFInfo
- Publication number
- CN102346129A CN102346129A CN2011101814997A CN201110181499A CN102346129A CN 102346129 A CN102346129 A CN 102346129A CN 2011101814997 A CN2011101814997 A CN 2011101814997A CN 201110181499 A CN201110181499 A CN 201110181499A CN 102346129 A CN102346129 A CN 102346129A
- Authority
- CN
- China
- Prior art keywords
- spectrum
- fingerprint image
- arch
- target
- summit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention provides a target identification method of gas extracted based on radiation spectrum characteristics. A fingerprint image is generated on a gas target spectrum by using a one-dimensional curvature scale space description method; fingerprint characteristics of the fingerprint image, namely the position of an arched peak, are extracted; and the closest position of the fingerprint characteristics is as the reference to carry out target matching identification. According to the method disclosed by the invention, steady radiation spectrum intrinsic characteristics which are not influenced by temperature and pressure are extracted; the method has robustness resisting condition change and good differentiating capability; and the support is provided for the future identification based on the spectrum under a complex condition.
Description
Technical field
The invention belongs to based on spectrum signature the time become the identification field, be specifically related to a kind of gas width of cloth and penetrate the spectrum signature method for distilling.
Background technology
Conventional detection means generally uses simple spectrum section imaging detection method; On the spectral coverage of broad, gather to be identified/background energy; This moment is to be identified often to be submerged among the complicated background clutter or hidden by interference, camouflage etc., and it is very faint to show as signal to be identified, and signal to noise ratio (S/N ratio), signal to noise ratio are very low.Yet intrinsic physics, chemical characteristic and the structure etc. of object to be identified have than big difference with background, interference, camouflage etc.; Therefore on some spectral line or narrower spectral coverage; Its characteristic can be apparent in view; Promptly on these spectral lines or narrower spectral coverage, signal to noise ratio (S/N ratio), the signal to noise ratio of the relative background of object to be identified are higher.Through the extraction of spectrum fingerprint, utilize the spectrum signature of the uniqueness on these spectral lines to be identified, can improve detectivity to be identified greatly.
Multispectral information to be identified, background is carried out effective choice, extraction, analysis; And spectrum information, image information and time-varying information merged; Can realize simplified system, distribute rationally, have complementary advantages, improve the reliability of detection to be identified, identification under the complex conditions greatly.
Along with increasing of wave band quantity, can select or extract the outstanding characteristic to be identified of specific wave band during detection to be identified as required, the selection of spectral signature becomes more flexibly with various.Simultaneously, contain abundant spectrum knowledge to be identified in the spectroscopic data, these spectrum knowledge can be converted into different character through different performances and array mode, can signature analysis space widely be provided for detection to be identified and identification.In addition, all kinds of atural objects all have spectral reflectance and the radiation characteristic of oneself, utilize the spectral signature of different atural objects, can realize the classification and the identification of atural object.Therefore, the spectrum fingerprint characteristic extracts and becomes the important content in the Study of recognition.
From the nineties in last century; The seminar of high spectrum image detection algorithm theoretical research to be identified has abroad appearred carrying out; Like Univ Maryland-Coll Park USA Baltimore branch school remote sensing signal and the breadboard Chang group of Flame Image Process, the Lincoln laboratory Manolakis of Massachusetts Institute Technology group or the like.So far; Use spectrum to discern and still exist a difficult problem (as shown in Figure 3): spectrum can change with the change of physical conditions such as body temperature to be identified, pressure. therefore, unique invariant features that extracts spectrum become based on the multispectral figure of spectrum fingerprint (as) to be identified and the research content that detects.
Summary of the invention
The object of the present invention is to provide a kind of gas width of cloth of resisting temperature pressure change to penetrate the spectrum signature method for distilling, for the identification under the complex conditions and detection provide excellent support.
A kind ofly penetrate the gas target identification method that spectrum signature is extracted based on the width of cloth, relate to the fingerprint picture library in order to coupling, spectrum fingerprint picture library comprises the fingerprint image that adopts one dimension curvature scale space describing method to generate respectively to a plurality of reference spectrums, and this method is specially:
(1) adopt one dimension curvature scale space describing method to generate fingerprint image to gas target spectrum;
(2) extract the fingerprint characteristic that target is composed all reference spectrum fingerprint images in fingerprint image and the fingerprint picture library respectively, said fingerprint characteristic is the two-dimensional coordinate value on arch summit in the fingerprint image;
(3) upgrade reference spectrum fingerprint image R
iThe Y-axis coordinate figure on all arch summits be itself and height correction parameter k
iProduct, wherein,
I=1,2 ..., N, N are the reference spectrum sum,
Be Y-axis coordinate figure maximum in the arch summit of i reference spectrum fingerprint image,
Be maximum Y-axis coordinate figure in the arch summit of target spectrum fingerprint image;
(4) in the reference spectrum fingerprint image after step (3) is upgraded, be close to the standard search most with the arch vertex position and compose the reference spectrum fingerprint image R that fingerprint image mates most with target
m, and record R
mEach arch summit different with its two-dimensional coordinate value difference respectively on the corresponding arch summit of target spectrum fingerprint image, the different summation of two-dimensional coordinate value difference that obtains is obtained mating cost:
(5) be the corresponding gas of R if the coupling cost that step (4) calculates, is then judged the gas target less than discrimination threshold.
Further, said step (4) is specially:
(41) all arch summit two-dimensional coordinate values of the reference spectrum fingerprint image after target spectrum fingerprint image and step (3) renewal being carried out normalization respectively handles;
(42) after normalization is handled, the arch summit T of maximum Y-axis coordinate figure in the target spectrum fingerprint image
oWith reference spectrum fingerprint image R
iIn the arch summit of maximum Y-axis coordinate figure
Constitute start node
I=1,2 ..., N;
(43) calculate T in the start node
oWith
The two-dimensional coordinate value difference different
Mark arch summit T
oWith
(44) from
I=1,2 ..., select reckling among the N
Its corresponding reference spectrum fingerprint image is designated as R
mIf target spectrum and R
mAll there is unmarked arch summit, gets into step (45), if the target spectrum is only arranged or reference spectrum fingerprint image R is only arranged
mThere is unmarked arch summit, gets into step (46), if target spectrum and reference spectrum fingerprint image R
mAll there is not unmarked arch summit, gets into step (47);
(45) respectively at target spectrum and reference spectrum fingerprint image R
mIn seek the maximum arch summit configuration node of Y-axis coordinate figure in the unlabelled arch summit, calculate the two-dimensional coordinate value discrepancy delta on two summits in this node, upgrade
Step (44) is returned on two arch summits in this node of mark;
(46) calculate target spectrum or reference spectrum fingerprint image R
mIn the two-dimensional coordinate value sum Δ on unmarked arch summit, upgrade
Step (44) is returned on these unmarked arch summits of mark;
(47) note R
mFor target is composed the reference spectrum fingerprint image that fingerprint image mates most,
For the coupling cost, finish.
Technique effect of the present invention is embodied in:
Can obtain the invariant features that accumulates in the spectrum effectively after the change on condition changes caused stave sight, to the condition influence of sample collection, complex conditions is discerned down and good basis is not established in detection in order to adapt to when not set up the spectrum database.The present invention proposes to compose the New Policy that fingerprint characteristic extracts, and solves the problem of extracting invariant features, provides support for discerning down based on the complex conditions of spectrum signature.
Description of drawings
Fig. 1 is a flow chart of steps of the present invention;
Fig. 2 is an effect process flow diagram of the present invention;
Fig. 3 is the variation of spectral line under different physical conditions, and Fig. 3 (a) penetrates spectrum for the width of cloth of atmospheric carbon dioxide under different temperatures; Fig. 3 (b) penetrates spectrum for the width of cloth of atmospheric carbon dioxide under different pressure;
Fig. 4 is that the spectrum signature of atmospheric carbon dioxide under temperature variation extracted the result. Fig. 4 (a) is the radiation spectrum under the atmospheric carbon dioxide different temperatures; Fig. 4 (b) material for this reason penetrates the spectrum fingerprint image of spectrum at the solid line width of cloth; On behalf of the width of cloth, Fig. 4 (c) material for this reason penetrate the spectrum fingerprint image of spectrum at dotted line; Fig. 4 (d) distributes for initial characteristics; Fig. 4 (e) is for revising the back characteristic distribution.
Fig. 5 is that the spectrum signature of allied substances under pressure change extracted the result. Fig. 5 (a) is the radiation spectrum under the different pressure of atmospheric carbon dioxide; Fig. 5 (b) width of cloth of representing at dotted line of material for this reason penetrates the spectrum fingerprint image of spectrum; Fig. 5 (c) width of cloth of representing at solid line of material for this reason penetrates the spectrum fingerprint image of spectrum; Fig. 5 (d) distributes for initial characteristics; Fig. 5 (e) is for revising the back characteristic distribution.
Fig. 6 extracts result in temperature with the spectrum signature under pressure changes simultaneously for allied substances. the radiation spectrum under the different physical states of Fig. 6 (a) carbon dioxide (temperature, pressure all change); The width of cloth that Fig. 6 (b) dotted line is represented is penetrated the fingerprint image of spectrum; The width of cloth that Fig. 6 (c) solid line is represented is penetrated the fingerprint image of spectrum; Fig. 6 (d) initial characteristics distributes; Fig. 6 (e) revises the back characteristic distribution.
Fig. 7 extracts the result for the spectrum signature of foreign peoples's material. and Fig. 7 (a) is the radiation spectrum of two kinds of different materials. and Fig. 7 (b) is dotted line representative spectrum fingerprint image; Fig. 7 (c) is solid line representative spectrum fingerprint image; Fig. 7 (d) distributes for initial characteristics; Fig. 7 (e) is for revising the back characteristic distribution.
Embodiment
Invariant features of the present invention is meant that under multispectral and ultra spectrum acquisition sensor condition, can obtain and spectrum signature relevant uniqueness to be identified, and this spectrum signature remains unchanged under different environmental conditions.
Treatment scheme of the present invention is specially as illustrated in fig. 1 and 2:
(1) use one dimension curvature scale space (CSS) to generate fingerprint image respectively to reference spectrum and spectrum to be identified.
1. create the fingerprint image of a sky, set up the two-dimensional coordinate axle, wherein X-axis is represented wave number, and Y-axis is to rear sight degree parameter;
2. select for use a less scale parameter value as the current scale parameter;
3. generate the one dimension gaussian kernel according to the current scale parameter, and the spectrum data be considered as the one dimension vector data corresponding with the current scale parameter the one dimension gaussian kernel carry out convolution algorithm;
4. the result who obtains after the convolution is asked second derivative, the positional information (comprising wave number and scale parameter) that record second derivative zero crossing occurs is marked on the fingerprint image correspondence position of establishment in 1. in step;
5. obtain bigger scale parameter, repeat step 3. to 4., till no longer producing new second derivative zero crossing.
(2) extract the initial fingerprint characteristic
Fingerprint image is some arches on apparent, is the initial fingerprint characteristic with height (being the Y-axis coordinate) and position (being the X-axis coordinate) of arch.
(3) initial characteristics correction
With in the reference spectrum fingerprint image the apex height of high arch be standard, in the spectrum fingerprint image to be identified the summit of high arch carry out high elongation, it is overlapped with the former. the computed altitude corrected parameter:
All dome height of spectrum fingerprint to be identified multiply by NormRatio and carry out normalization.
(4) the coupling cost is calculated
In the reference spectrum fingerprint image after step (3) is upgraded, be close to the standard search most with the arch vertex position and compose the reference spectrum fingerprint image R that fingerprint image mates most with target
m, and record R
mEach arch summit different with its two-dimensional coordinate value difference respectively on the corresponding arch summit of target spectrum fingerprint image, the different summation of two-dimensional coordinate value difference that obtains is obtained mating cost.
The simplest a kind of directly method is exactly to calculate the coupling cost of all fingerprint images in spectrum fingerprint image to be identified and the target spectrum fingerprint picture library respectively, and the target spectrum fingerprint image of the minimum correspondence of coupling cost is the final matching cost.
In practical operation, in order to improve computing velocity, the implementation algorithm performance optimization can adopt the strategy of following depth-first search to accomplish.
The coupling cost of two fingerprint images of the present invention is to obtain after the balanced cost algorithms of special case (Uniform Cost Algorithm) of employing A* algorithm is improved.The function of this algorithm is that all or part of arch in the whole arches in the fingerprint image (template figure) and another fingerprint image (target figure) is carried out optimum matching.Algorithm steps is following:
If fingerprint image to be identified is O, the template base fingerprint image is R
i, i=1,2 ..., N coupling task is specially at i=1 2 ..., the most close fingerprint image in arch summit with O is sought on the arch summit on the whole among the N.
(41) to all fingerprint images (be O, i=1,2 ..., XY axle N) is normalized to 0 to 1 respectively, and the coupling task begins from higher arch; In practical operation, can ignore highly too small arch, its objective is the interference of removing factors such as noise. it is 5% (desirable scope 1%-10%) that arch filtration threshold value can be set, and promptly height will be left in the basket less than the highest arch 5%.
(42) create initial node.The arch summit T of maximum Y-axis coordinate figure in the target spectrum fingerprint image
oWith reference spectrum fingerprint image R
iIn the arch summit of maximum Y-axis coordinate figure
Constitute start node
I=1,2 ..., N.Because 3 fingerprint images are arranged in the template base, so O and i=1,2 ..., the N symbiosis becomes N initial node.
(43) the coupling cost of calculating initial node. what mate cost is calculated as two pairing arch summits respectively at X, the difference sum of Y-axis. and the arch to having matched is carried out mark.
An example is described below.
If fingerprint image to be identified is A, reference fingerprint figure is B
1, B
2, B
3The arch apex coordinate is (0.2,0.4) among the A, (0.6,0.9) and (0.9,0.2) .B
1Middle arch apex coordinate is (0.1,0.4) and (0.8,0.6), B
2Middle arch apex coordinate is (0.2,0.5), (0.2,0.7) and (0.8,0.2), B
3Middle arch apex coordinate is (0.4,0.7), (0.5,0.3), (0.8,0.4) and (0.9,0.5).
The highest arch apex coordinate is (0.6,0.9) among the A, B
1, B
2, B
3In the highest arch summit sit and to be respectively (0.8,0.6), (0.4,0.7) and (0.2,0.7), among the A the highest arch summit respectively with B
1, B
2, B
3In the highest arch summit create initial node, calculating is mated on the above arch of mark summit.If two arch apex coordinates are respectively (x
1, y
1) and (x
2, y
2), coupling cost computing formula does
MC＝abs(x
2-x
1)+abs(y
2-y
1)
Abs () expression takes absolute value, and calculates node AB according to following formula
1The coupling cost is 0.5, AB
2The coupling cost is 0.6, AB
3The coupling cost is 0.4.Node AB
3The coupling cost is minimum, it is expanded, at A and B
3In seek in the unlabelled arch summit the highest a pair ofly, carry out step in claims (44) to (47), finally obtain AB
2The coupling cost is 0.6.
(5) identification is differentiated
When mating cost less than discrimination threshold, object to be identified is judged to target; Otherwise; Object to be identified be judged to non-target .. wherein threshold value be empirical value; General way is that all samples of getting in a target to be identified and the target fingerprint picture library mate calculating, will obtain the maximal value of a coupling cost, and threshold value is got the 1%-10% of maximum match cost.
Enumerate several instances below.
Instance one
Carry out the fingerprint image matching test of radiation spectrum under the allied substances different temperatures in this example.
Choose the width of cloth of atmospheric carbon dioxide under different temperatures penetrate the spectrum data test. the spectrum data shown in Fig. 4 (a).Fig. 4 (a) is the radiation spectrum under the atmospheric carbon dioxide different temperatures, and wherein to represent atmospheric carbon dioxide be that the width of cloth under the 296K is penetrated spectrum in temperature to solid line, and it is that the width of cloth under the 460K is penetrated spectrum in temperature that dotted line is represented atmospheric carbon dioxide.
The first step: generate the CSS fingerprint image.
1. create the fingerprint image of a sky, X-axis is a wave number, and Y-axis is a scale parameter;
2. setting initial gaussian kernel scale parameter is 1, and the increment step-length of scale parameter is 0.5;
3. generate the one dimension gaussian kernel according to the current scale parameter;
4. the one dimension gaussian kernel that obtains in the spectrum data vector and 3 is carried out convolution algorithm; Result to obtaining after the convolution asks second derivative, the position that record second derivative zero crossing occurs. and the fingerprint image Y-axis of in 1, creating (being scale parameter) is the beam location that 1 local mark second derivative zero crossing occurs.
5. treat the scale parameter of computing according to the increment step size computation next one of scale parameter, repeat 3 and 4, until no longer producing new second derivative zero crossing.
6. fingerprint image generates. and the result is shown in Fig. 4 (b) and 4 (c).
Second step: extract the initial fingerprint characteristic.
Fingerprint image is some arches on apparent, is the initial fingerprint characteristic with height (being the Y-axis coordinate) and position (being the X-axis coordinate) of arch. the formation of arch is the process that is faded away along with the increase of scale parameter by a pair of second derivative zero crossing.
Result of calculation shown in table 1 and 2, initial fingerprint characteristic such as Fig. 4 (d), round dot is represented spectrum to be identified, Fang Dian represents target spectrum.Table 1 reference spectrum fingerprint characteristic
| X (wave number) | 0.4571 | 0.2857 | 0.8266 | 0.1357 |
| Y (yardstick) | 0.8400 | 0.2800 | 0.3600 | 0.1000 |
Table 2 spectrum fingerprint beginning to be identified characteristic:
| X (wave number) | 0.5000 | 0.2786 | 0.8429 | 0.12869 |
| Y (yardstick) | 0.5000 | 0.2786 | 0.8429 | 0.1286 |
The 3rd step: initial characteristics correction.
With in the reference spectrum fingerprint image the apex height of high arch be standard, in the spectrum fingerprint image to be identified the summit of high arch carry out high elongation, it is overlapped with the former.Calculate normalized parameter:
All dome height of spectrum fingerprint to be identified multiply by NormRatio and carry out normalization.
Result of calculation is shown in table 3 and 4, and initial characteristics distributes like Fig. 4 (e), and round dot is represented spectrum to be identified, and Fang Dian represents the target spectrum.
The revised reference spectrum fingerprint characteristic of table 3
| X (wave number) | 0.4571 | 0.2857 | 0.8286 | 0.1357 |
| Y (yardstick) | 0.8400 | 0.2800 | 0.3600 | 0.1000 |
Characteristic after the table 4 spectrum correction to be identified:
| X (wave number) | 0.3571 | 0.2786 | 0.8429 | 0.1286 |
| Y (yardstick) | 0.8400 | 0.2809 | 0.3326 | 0.1800 |
The 4th step: calculate the coupling cost.
Calculating the coupling cost according to the balanced cost algorithms of the special case of A* algorithm (Uniform Cost Algorithm) is 0.40.
The 5th step: identification is differentiated.
The coupling cost is less than threshold value 1, and spectrum to be identified is judged to carbon dioxide.
Instance two
Carry out the fingerprint image matching test of radiation spectrum under the different pressure of allied substances in this example.
Choose the width of cloth of atmospheric carbon dioxide under different pressure penetrate the spectrum data test. the spectrum data shown in Fig. 5 (a).Fig. 5 (a) is the radiation spectrum under the different pressure of atmospheric carbon dioxide, and wherein to represent atmospheric carbon dioxide be that the width of cloth under the 0.1MPa is penetrated spectrum at pressure to solid line, and it is that the width of cloth under the 0.2MPa is penetrated spectrum at pressure that dotted line is represented atmospheric carbon dioxide.
The first step: generate the CSS fingerprint image.
Reference spectrum and spectrum to be identified are carried out step 1 respectively to 6.
1. create the fingerprint image of a sky, X-axis is a wave number, and Y-axis is a scale parameter;
2. setting initial gaussian kernel scale parameter is 1, and the increment step-length of scale parameter is 0.5;
3. generate the one dimension gaussian kernel according to the current scale parameter;
4. the one dimension gaussian kernel that obtains in the spectrum data vector and 3 is carried out convolution algorithm; Result to obtaining after the convolution asks second derivative, the position that record second derivative zero crossing occurs. and the fingerprint image Y-axis of in 1, creating (being scale parameter) is the beam location that 1 local mark second derivative zero crossing occurs.
5. treat the scale parameter of computing according to the increment step size computation next one of scale parameter, repeat 3 and 4, until no longer producing new second derivative zero crossing.
6. fingerprint image generates. and the result is shown in Fig. 5 (b) and 5 (c).
Second step: extract the initial fingerprint characteristic.
Fingerprint image is some arches on apparent, is the initial fingerprint characteristic with height (being the Y-axis coordinate) and position (being the X-axis coordinate) of arch. the formation of arch is the process that is faded away along with the increase of scale parameter by a pair of second derivative zero crossing.
Result of calculation is shown in table 6 and 7, and initial characteristics distributes and represents spectrum to be identified like Fig. 5 (d) round dot, and Fang Dian represents the target spectrum.
Table 5 reference spectrum fingerprint characteristic
| X (wave number) | 0.4571 | 0.2929 | 0.8286 |
| Y (yardstick) | 0.8240 | 0.3600 | 0.3000 |
Table 6 spectrum fingerprint characteristic to be identified
| X (wave number) | 0.4714 | 0.2857 | 0.8214 | 0.1371 |
| Y (yardstick) | 0.9080 | 0.3240 | 0.3040 | 0.0880 |
The 3rd step: initial characteristics correction.
With in the reference spectrum fingerprint image the apex height of high arch be standard, in the spectrum fingerprint image to be identified the summit of high arch carry out high elongation, it is overlapped with the former. calculate normalized parameter:
All dome height of spectrum fingerprint to be identified multiply by NormRatio and carry out normalization.
Result of calculation shown in table 8 and 9, initial characteristics such as Fig. 5 (e), round dot is represented spectrum to be identified, Fang Dian represents target spectrum.
The revised reference spectrum fingerprint characteristic of table 7
| X (wave number) | 0.4571 | 0.2929 | 0.8543 |
| Y (yardstick) | 0.8240 | 0.3600 | 0.3000 |
The revised spectrum fingerprint characteristic to be identified of table 8:
| X (wave number) | 0.4714 | 0.2857 | 0.8214 | 0.1371 |
| Y (yardstick) | 0.8240 | 0.2940 | 0.2758 | 0.0798 |
The 4th step: calculate the coupling cost.
Calculating the coupling cost according to the balanced cost algorithms of the special case of A* algorithm (Uniform Cost Algorithm) is 0.45.
The 5th step: identification is differentiated.
The coupling cost is less than threshold value 1, and spectrum to be identified is judged to carbon dioxide.
Instance three
Carry out allied substances temperature and pressure in this example and change the fingerprint image matching test of radiation spectrum down simultaneously.Choose the width of cloth of atmospheric carbon dioxide when different temperatures and pressure penetrate the spectrum data test. the spectrum data shown in Fig. 6 (a).Radiation spectrum under the different physical states of Fig. 6 (a) carbon dioxide (temperature, pressure all change). wherein to represent carbon dioxide be 172K in temperature to solid line, and pressure is the radiation spectrum under the 0.1MPa; It is 296K in temperature that dotted line is represented carbon dioxide, and pressure is the radiation spectrum under the 0.2MPa.
The first step: generate the CSS fingerprint image.
Reference spectrum and spectrum to be identified are carried out step 1 respectively to 6.
1. create the fingerprint image of a sky, X-axis is a wave number, and Y-axis is a scale parameter;
2. setting initial gaussian kernel scale parameter is 1, and the increment step-length of scale parameter is 0.5;
3. generate the one dimension gaussian kernel according to the current scale parameter;
4. the one dimension gaussian kernel that obtains in the spectrum data vector and 3 is carried out convolution algorithm; Result to obtaining after the convolution asks second derivative, the position that record second derivative zero crossing occurs. and the fingerprint image Y-axis of in 1, creating (being scale parameter) is the beam location that 1 local mark second derivative zero crossing occurs.
5. treat the scale parameter of computing according to the increment step size computation next one of scale parameter, repeat 3 and 4, until no longer producing new second derivative zero crossing.
6. fingerprint image generates. and the result is shown in Fig. 6 (b) and 6 (c).
Second step: extract the initial fingerprint characteristic.
Fingerprint image is some arches on apparent, is the initial fingerprint characteristic with height (being the Y-axis coordinate) and position (being the X-axis coordinate) of arch. the formation of arch is the process that is faded away along with the increase of scale parameter by a pair of second derivative zero crossing.
Result of calculation is shown in table 9 and 10, and initial characteristics distributes like Fig. 6 (d), red representative spectrum to be identified, the blue target spectrum of representing.
Table 9 reference spectrum initial characteristics
| X (wave number) | 0.48 | 0.27 | 0.84 | 0.13 |
| Y (yardstick) | 0.83 | 0.23 | 0.21 | 0.06 |
Table 10 spectrum initial characteristics to be identified:
| X (wave number) | 0.49 | 0.28 | 0.82 |
| Y (yardstick) | 0.91 | 0.23 | 0.20 |
The 3rd step: initial characteristics correction.
With in the reference spectrum fingerprint image the apex height of high arch be standard, in the spectrum fingerprint image to be identified the summit of high arch carry out high elongation, it is overlapped with the former. calculate normalized parameter:
All dome height of spectrum fingerprint to be identified multiply by NormRatio and carry out normalization.
Result of calculation shown in table 11 and 12, initial characteristics such as Fig. 6 (e), red representative spectrum to be identified, the blue target of represent is composed.
Table 11 reference spectrum correction characteristic
| X (wave number) | 0.4857 | 0.2786 | 0.8429 | 0.1357 |
| Y (yardstick) | 0.8333 | 0.2333 | 0.2167 | 0.0667 |
Table 12 spectrum correction to be identified characteristic:
| X (wave number) | 0.4929 | 0.2857 | 0.8286 |
| Y (yardstick) | 0.8333 | 0.2030 | 0.1848 |
The 4th step: calculate the coupling cost.
Calculating the coupling cost according to the balanced cost algorithms of the special case of A* algorithm (Uniform Cost Algorithm) is 2.6.
The 5th step: identification is differentiated.
The coupling cost less than threshold value 1, spectrum to be identified be judged to reference spectrum be carbon dioxide.
Instance four
Carry out the fingerprint image matching test of foreign peoples's material radiation spectrum in this example.The width of cloth of choosing the mixed gas that atmospheric carbon dioxide and aviation kerosene JET A-1 burning produces is penetrated the spectrum data and is tested. and the spectrum data are shown in Fig. 7 (a).Fig. 7 (a) is the radiation spectrum of two kinds of different materials, and wherein on behalf of aviation kerosene JETA-1 open burning, solid line produce the radiation spectrum of mixed gas; It is 296K in temperature that dotted line is represented carbon dioxide, and pressure is that the width of cloth under the 0.2MPa is penetrated spectrum.
The first step: generate the CSS fingerprint image.
Reference spectrum and spectrum to be identified are carried out step 1 respectively to 6.
1. create the fingerprint image of a sky, X-axis is a wave number, and Y-axis is a scale parameter;
2. setting initial gaussian kernel scale parameter is 1, and the increment step-length of scale parameter is 0.5;
3. generate the one dimension gaussian kernel according to the current scale parameter;
4. the one dimension gaussian kernel that obtains in the spectrum data vector and 3 is carried out convolution algorithm; Result to obtaining after the convolution asks second derivative, the position that record second derivative zero crossing occurs. and the fingerprint image Y-axis of in 1, creating (being scale parameter) is the beam location that 1 local mark second derivative zero crossing occurs.
5. treat the scale parameter of computing according to the increment step size computation next one of scale parameter, repeat 3 and 4, until no longer producing new second derivative zero crossing.
6. fingerprint image generates. and the result is shown in Fig. 7 (b) and 7 (c).
Second step: extract the initial fingerprint characteristic.
Fingerprint image is some arches on apparent, is the initial fingerprint characteristic with height (being the Y-axis coordinate) and position (being the X-axis coordinate) of arch. the formation of arch is the process that is faded away along with the increase of scale parameter by a pair of second derivative zero crossing.
Result of calculation is shown in table 13 and 14, and initial characteristics distributes like Fig. 7 (d), and round dot is represented spectrum to be identified, and Fang Dian represents the target spectrum.
Table 13 reference spectrum fingerprint characteristic
| X (wave number) | 0.4571 | 0.2857 | 0.8286 | 0.1357 |
| Y (yardstick) | 0.7000 | 0.2333 | 0.3000 | 0.0833 |
Table 14 spectrum fingerprint beginning to be identified characteristic:
| X (wave number) | 0.5429 | 0.2571 | 0.8786 | 0.2286 |
| Y (yardstick) | 0.9000 | 0.2333 | 0.0833 | 0.0667 |
The 3rd step: initial characteristics correction.
With in the reference spectrum fingerprint image the apex height of high arch be standard, in the spectrum fingerprint image to be identified the summit of high arch carry out high elongation, it is overlapped with the former. calculate normalized parameter:
All dome height of spectrum fingerprint to be identified multiply by NormRatio and carry out normalization.
Result of calculation shown in table 15 and 16, initial characteristics such as Fig. 7 (e), round dot is represented spectrum to be identified, Fang Dian represents target spectrum.
The revised reference spectrum fingerprint characteristic of table 15
| X (wave number) | 0.4571 | 0.2857 | 0.8286 | 0.1357 |
| Y (yardstick) | 0.7000 | 0.2333 | 0.3000 | 0.0833 |
The revised spectrum fingerprint characteristic to be identified of table 16:
| X (wave number) | 0.5429 | 0.2571 | 0.8786 | 0.2286 |
| Y (yardstick) | 0.7000 | 0.1815 | 0.0648 | 0.0519 |
The 4th step: calculate the coupling cost.
Calculating the coupling cost according to the balanced cost algorithms of the special case of A* algorithm (Uniform Cost Algorithm) is 3.6.The 5th step: identification is differentiated.
The coupling cost greater than threshold value 1, spectrum to be identified be judged to reference spectrum be foreign peoples's material.
To sum up instance one to four is visible, and the feature extraction that the present invention is used for the gas radiation spectrum all is not subjected to the influence of temperature and pressure change, and has good material and distinguish and separating capacity.
Claims (2)
1. penetrate the gas target identification method that spectrum signature is extracted based on the width of cloth for one kind, relate to the fingerprint picture library in order to coupling, spectrum fingerprint picture library comprises the fingerprint image that adopts one dimension curvature scale space describing method to generate respectively to a plurality of reference spectrums, and this method is specially:
(1) adopt one dimension curvature scale space describing method to generate fingerprint image to gas target spectrum;
(2) extract the fingerprint characteristic that target is composed all reference spectrum fingerprint images in fingerprint image and the fingerprint picture library respectively, said fingerprint characteristic is the two-dimensional coordinate value on arch summit in the fingerprint image;
(3) upgrade reference spectrum fingerprint image R
iThe Y-axis coordinate figure on all arch summits be itself and height correction parameter k
iProduct, wherein,
I=1,2 ..., N, N are the reference spectrum sum,
Be Y-axis coordinate figure maximum in the arch summit of i reference spectrum fingerprint image,
Be maximum Y-axis coordinate figure in the arch summit of target spectrum fingerprint image;
(4) in the reference spectrum fingerprint image after step (3) is upgraded, be close to the standard search most with the arch vertex position and compose the reference spectrum fingerprint image R that fingerprint image mates most with target
m, and record R
mEach arch summit different with its two-dimensional coordinate value difference respectively on the corresponding arch summit of target spectrum fingerprint image, the different summation of two-dimensional coordinate value difference that obtains is obtained mating cost:
(5) be the corresponding gas of R if the coupling cost that step (4) calculates, is then judged the gas target less than discrimination threshold.
2. according to the said gas target identification method of claim 1, it is characterized in that said step (4) is specially:
(41) all arch summit two-dimensional coordinate values of the reference spectrum fingerprint image after target spectrum fingerprint image and step (3) renewal being carried out normalization respectively handles;
(42) after normalization is handled, the arch summit T of maximum Y-axis coordinate figure in the target spectrum fingerprint image
oWith reference spectrum fingerprint image R
iIn the arch summit of maximum Y-axis coordinate figure
Constitute start node
I=1,2 ..., N;
(43) calculate T in the start node
oWith
The two-dimensional coordinate value difference different
Mark arch summit T
oWith
(44) from
I=1,2 ..., select reckling among the N
Its corresponding reference spectrum fingerprint image is designated as R
mIf target spectrum and R
mAll there is unmarked arch summit, gets into step (45), if the target spectrum is only arranged or reference spectrum fingerprint image R is only arranged
mThere is unmarked arch summit, gets into step (46), if target spectrum and reference spectrum fingerprint image R
mAll there is not unmarked arch summit, gets into step (47);
(45) respectively at target spectrum and reference spectrum fingerprint image R
mIn seek the maximum arch summit configuration node of Y-axis coordinate figure in the unlabelled arch summit, calculate the two-dimensional coordinate value discrepancy delta on two summits in this node, upgrade
Step (44) is returned on two arch summits in this node of mark;
(46) calculate target spectrum or reference spectrum fingerprint image R
mIn the two-dimensional coordinate value sum Δ on unmarked arch summit, upgrade
Step (44) is returned on these unmarked arch summits of mark;
(47) note R
mFor target is composed the reference spectrum fingerprint image that fingerprint image mates most,
For the coupling cost, finish.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110181499 CN102346129B (en) | 2011-06-30 | 2011-06-30 | Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110181499 CN102346129B (en) | 2011-06-30 | 2011-06-30 | Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102346129A true CN102346129A (en) | 2012-02-08 |
| CN102346129B CN102346129B (en) | 2013-03-27 |
Family
ID=45545004
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110181499 Expired - Fee Related CN102346129B (en) | 2011-06-30 | 2011-06-30 | Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102346129B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6424745B1 (en) * | 1998-05-19 | 2002-07-23 | Lucent Technologies Inc. | Method and apparatus for object recognition |
| CN101853503A (en) * | 2010-04-26 | 2010-10-06 | 华中科技大学 | Spectral line inflexion multi-scale optimizing segmentation method and application thereof |
| CN101976337A (en) * | 2010-10-27 | 2011-02-16 | 华中科技大学 | Method for extracting stable spectrum characteristics of solid matter |
-
2011
- 2011-06-30 CN CN 201110181499 patent/CN102346129B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6424745B1 (en) * | 1998-05-19 | 2002-07-23 | Lucent Technologies Inc. | Method and apparatus for object recognition |
| CN101853503A (en) * | 2010-04-26 | 2010-10-06 | 华中科技大学 | Spectral line inflexion multi-scale optimizing segmentation method and application thereof |
| CN101976337A (en) * | 2010-10-27 | 2011-02-16 | 华中科技大学 | Method for extracting stable spectrum characteristics of solid matter |
Non-Patent Citations (2)
| Title |
|---|
| 范璐 等: "大豆油和花生油傅里叶变换红外吸收光谱识别分析", 《河南工业大学学报（自然科学版）》 * |
| 赵小蓉 等: "药品质量稳定性的傅立叶变换红外吸收光谱识别分析", 《分析测试学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102346129B (en) | 2013-03-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xia et al. | Hyperspectral image classification with canonical correlation forests | |
| CN107451614B (en) | Hyperspectral classification method based on fusion of space coordinates and space spectrum features | |
| Feng et al. | Robust and efficient algorithms for separating latent overlapped fingerprints | |
| CN109191502B (en) | Method for automatically identifying cartridge case trace | |
| CN109858477A (en) | The Raman spectrum analysis method of object is identified in complex environment with depth forest | |
| CN103473545B (en) | A kind of text image method for measuring similarity based on multiple features | |
| CN101968850A (en) | Method for extracting face feature by simulating biological vision mechanism | |
| CN107292258A (en) | High spectrum image low-rank representation clustering method with filtering is modulated based on bilateral weighted | |
| Tong et al. | An improved multiobjective discrete particle swarm optimization for hyperspectral endmember extraction | |
| CN107463895B (en) | Small and weak damage object detection method based on neighborhood vector PCA | |
| CN110569884A (en) | Hyperspectral remote sensing image classification method based on deep learning and morphology | |
| CN111027509A (en) | Hyperspectral image target detection method based on double-current convolution neural network | |
| CN112907520A (en) | Single tree crown detection method based on end-to-end deep learning method | |
| CN111783884A (en) | Unsupervised hyperspectral image classification method based on deep learning | |
| Zhang et al. | An improved feature set for hyperspectral image classification: Harmonic analysis optimized by multiscale guided filter | |
| Pu | Detecting and Mapping Invasive Plant Species Using Hyperspectral Data | |
| CN113159189A (en) | Hyperspectral image classification method and system based on double-branch multi-attention convolutional neural network | |
| Sawarkar et al. | A review: Rose plant disease detection using image processing | |
| Chaki et al. | Plant leaf recognition using Gabor filter | |
| Negri et al. | Exploring the capability of ALOS PALSAR L-band fully polarimetric data for land cover classification in tropical environments | |
| CN102346129B (en) | Gas radiation spectrum invariant characteristic extraction method suitable for temperature pressure change | |
| Hassaïne et al. | An online signature verification system for forgery and disguise detection | |
| Zhao et al. | Gabor-modulated grouped separable convolutional network for hyperspectral image classification | |
| CN101976337B (en) | Method for extracting stable spectrum characteristics of solid matter | |
| CN114527088A (en) | Folium artemisiae argyi producing area tracing method based on infrared spectrum fingerprint technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130327 Termination date: 20180630 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |