CN105426881B - Mountain background thermal field model constrained underground heat source daytime remote sensing detection locating method - Google Patents
Mountain background thermal field model constrained underground heat source daytime remote sensing detection locating method Download PDFInfo
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Abstract
The invention discloses a mountain background thermal field model constrained underground heat source daytime remote sensing detection locating method, belongs to the field of intersection of remote sensing technology, natural geography and mode identification, and aims at performing thermal field simulation of a mountain background to obtain a mountain background thermal field model and performing background filtering on an original remote sensing infrared image with the thermal field model acting as the constraint so as to clearly reveal the location of an underground target. The mountain background thermal field model constrained underground heat source daytime remote sensing detection locating method includes a mountain background thermal field model establishment step, a mountain background thermal field 8-bit-to-16-bit mapping step, a background filtering step by utilizing the mountain background thermal field model after mapping, and an underground target space constraint mean clustering detection locating step. The model is constructed by utilizing the mountain background thermal field, and gray scale mapping is performed on the model by utilizing the real mountain background so that the established thermal field model is ensured to be close to the real mountain background thermal field, and finally background filtering processing is performed on the real remote sensing infrared image and the location of the underground target is determined.
Description
Technical field
The invention belongs to the crossing domain of remote sensing technology, physical geography and pattern recognition, and in particular to a kind of massif background
Remote sensing localization method between the underground thermal source daytime of thermal field model constraint.
Background technology
The development of the mankind be unable to do without various natural resourcess, in order to obtain abundant subterranean resource, mineral resources and underground
Water resource etc., needs to build substantial amounts of underground installation, thus the Detection Techniques of the buried target such as hypogee, underground installation seem
Become more and more important.All temperature can produce heat radiation higher than the object of absolute zero, and buried target and peripheral region have difference
Thermodynamic behaviour, in the presence of external environment, the presence of buried target can affect the inside heat transfer process of peripheral region,
Cause to exist where buried target and time dependent temperature contrast occurs in ambient background.From statistical significance, i.e.,
Temperature field of the temperature field of buried target higher or lower than massif background.Therefore, it can regard as these buried targets and be different from
The underground thermal source of background thermal source.Compared to other detection means, the detection of infrared technique means has certain advantage.Non- remote sensing is visited
Survey means cannot realize large-area simultaneous observation, be difficult with rugged environment, and the speed for obtaining information is slow, and time-consuming
Arduously.Existing conventional remote sensing means obtain information and are subject to nature mainly for the conditional object in earth's surface or the water surface
The restriction of environment, background environment, it is impossible to detect deeper subsurface thermal source target.At present, electromagnetically induced technology may only detect shallow
Metal target under layer earth's surface, while easily being affected by the underground metal fragment that is scattered.Therefore, infrared technique means are detected into
For a kind of effective underground objects detection means.
At present, the detection both at home and abroad to buried target also has certain research.The country mainly concentrates on shallow-layer target
Detection, and for the detection of target under multi-temporal image, and these buried targets are mostly the underground thermals source of large scale.It is multinational
The relevant report of deep layer (being more than 10m away from surface distance) underground thermal source target acquisition is inside also had no, especially the underground of little yardstick
Thermal source target.Foreign countries have sweeps the research of sensor Underground target using airborne medium wave and long-wave infrared, but has no profit
The relevant report of underground objects detection is carried out with remote sensing images.For the detection of the Band object of plane, existing pattern recognition
Method is not using the method for background filtering.And it detect be only suspected target area, be not accurately positioned ground
Lower target position.Moreover, the false dismissed rate in suspected target area that detection is obtained is undesirable, and false alarm rate is high, depending on level
Exactness is not also high.
The content of the invention
The present invention is proposed under a kind of massif background heat field model constraint, the ground to deep layer (being more than 10m away from surface distance)
Under between distributed thermal source daytime Detection location method, solve the problems, such as it is existing just for shallow underground thermal source Detection location,
The analog simulation of thermal field is carried out to massif using simulation softwares, analysis obtains the thermal field model of massif body background, utilizes
True infrared figure carries out mapping unification to massif thermal field model, and carries out background to true infrared image using the model after mapping
Filtering Processing, reduces impact of the massif body background thermal field to detecting, and is finally accurately positioned the position at buried target place.
The present invention provides Detection location side between a kind of underground distributed thermal source daytime based on the constraint of massif background heat field model
Method.When setting up massif background heat field model, appropriate simplification is done according to actual massif thermal field, the model being simplified, concrete step
It is rapid as follows：
(1) foundation of massif background heat field model, including following sub-step：
(1.1) establishment step of mountain model
(1.1.1) the 3-dimensional digital elevation model for obtaining massif is surveyed and drawn by remote sensing, obtains the height above sea level of true massif
Data message.
(1.1.2) structure of ANSYS geometry mountain model is constituted with point, line, surface and body, and point (coordinate) is to build
The basis of geometric model.So according to above-mentioned altitude information, the establishment of geometry massif is the curve that closure is generated by key point,
The curve of closure generates plane, then surrounds geometry massif by the curved surface for closing.And geometry massif just constitutes whole massif mould
Type.
(1.2) the FEM meshing step of mountain model
Anasy FEM meshings are to carry out the vital step of numerical simulation analysis, due to massif model simultaneously
It is not regular, therefore carries out free mesh, is free to automatically generate triangle or tetrahedron net on the whole
Lattice, automatically generate tetrahedral grid on body, while manually carrying out the control of smart dimensions.
(1.3) mountain model boundary condition is arranged and solution procedure
The setting of load boundary condition is carried out to the massif after above-mentioned stress and strain model, using massif conduction of heat and massif-sky
The basic heat transfer physical basis of gas thermal convection current, according to pyroconductivity K of massif and thermal convection current rate Φ of massif-air heat is arranged
The parameter of transmission, through the solution of Ansys the thermo parameters method of massif is calculated, and finally the thermo parameters method of massif is carried out
The mapping of gray-scale maps and the adjustment of temperature resolution, finally give massif thermal field model.
(2) massif background thermal field 8 is to 16 bit mappings, including following sub-step：
Because the infrared thermal field model image of massif background set up is 8, and real massif background heat field picture is 16
Position, so needing the mapping that 8 to 16 are carried out to massif background heat field model obtained above and true massif background thermal field
Process.In the case where the thermal field of model is roughly the same with the change of real heterogeneity phantom, it is ensured that model thermal field is more nearly very
The thermal field of real massif.
(2.1) the determination step of true massif background gray levels scope
The thermal field of true massif background is affected by many extraneous factors, such as house, road etc..It is extraneous due to these
The shared ratio very little in whole background of factor, therefore interference can be considered as.So needing to carry out range constraint process, lead to
Statistics with histogram is crossed, threshold process is carried out, the interference of extraneous factor is rejected, detailed process is as follows：
(2.1.1) pixel grayscale in variable r representative images is set, under discrete situation, r is usedkRepresent discrete gray levels,
With P (rk) probability density function is represented, there is following formula to set up:
K=0,1,2...l-1
N in formulakTo there is r in imagekThe pixel count of gray scale, n is pixel count sum in image,In being exactly theory of probability
Frequency, l is the total number of gray level.
It is P% that known extraneous factor affects to account for occupied area ratio in entire image, then have following formula：
Pr(rk)≥P
Add up grey level histogram successively, if aggregate-value is more than or equal to object proportion, stop cumulative, record rk
Value, as the standard of background.
(2.2) the map correction step of massif thermal field model
The gray value of massif thermal field Model Background is linearly reflected according to the minimum gray value of value obtained above and image
Penetrate correction process.Specific formula is as follows：
Wherein, I for thermal field model gray value, IlFor the minimum brightness gray value of thermal field model, IhFor thermal field model most
High brightness gray value, OlFor the minimum brightness gray value of true massif infrared image, OhFor the above-mentioned r for trying to achievek, O is map correction
Massif background model afterwards.
(3) background filter step is carried out using the massif background heat field model after mapping
Angle and the impact of solar illumination angle due to remote sensor observation, can be divided into sunny side and the back by massif.The sun is straight
The massif part for connecing irradiation is referred to as sunny side, the sun cannot the part of direct irradiation be referred to as the back.Massif background and surrounding have
Heat radiation when heat radiation, direct irradiation and non-immediate irradiation has differences.Moreover, the thermal field of massif background and night between daytime
Between massif background thermal field it is also different, the present invention is directed massif background heat field model constraint underground thermal source daytime between detect
Positioning.The pixel of the boundary of the back and sunny side is found first, using the method fitting back and sunny side of least square fitting
Cut-off rule, then grey level compensation operation is carried out to the back, finally carry out background Filtering Processing.
(3.1) original infrared image back sunny side demarcation line extraction step
(3.1.1) pixel of boundary is determined using the difference of the back and sunny side gray scale difference value, according to view data
Information can determine that demarcation line is east-west, so more upper and lower pixel gray value is only needed, if neighbouring up and down
Pixel grey scale difference be more than K, formula is as follows：
G(x,y)>G(x,y-1)+K
G(x,y)>G(x,y-2)+K
G(x,y)>G(x,y-3)+K
G(x,y)>G(x,y-1)+K
If aforementioned four inequality is set up, just (x, y) is considered as into the pixel near demarcation line, traversal full figure is divided
All pixels point near boundary line.
(3.1.2) next cubic polynomial least square is carried out to the pixel near all demarcation line obtained above
Method linear process, detailed process is as follows：
WhereinFor the cubic polynomial of least square fitting, err is error target function, is reached by making err minimums
It is fitted to optimum cubic polynomial, obtains final demarcation line.
(3.2) the step of eliminating sunny side sun direct irradiation energy
After obtaining infrared image back region and sunny side region according to demarcation line, using following mapping policy to infrared
The gray value in image infrared image sunny side region is eliminated：
D=Fn'oshadow(i,j)-Fnoshadow(i,j)
F' in formulanoshadow(i, j) is the sunny side area grayscale value of the non-direct irradiation of the infrared image sun, Fnoshadow(i,j)
It is infrared image sun direct irradiation sunny side area grayscale value, mshadowAnd σshadowIt is the equal of infrared image sunny side area grayscale value
Value and variance, mnoshadowAnd σnoshadowIt is the average and variance of neighbouring non-infrared image infrared image back area grayscale value, A
For compensation intensity coefficient, D is the energy gray value of sun direct irradiation sunny side.
After the gray value of sun direct irradiation impact is obtained from above-mentioned, all the moon for detecting are traveled through
Face region, by infrared image sunny side region the gray value compensation D of each point of infrared figure is deducted, and be eliminated the sun
Gray-scale maps after irradiation impact.The impact for shining upon is not accounted for before, the component for shining upon is filtered, filter the sun
The energy that sunny side is shined upon, shines upon the thermal for causing, and is absorbed by sunny side, under thermal equilibrium condition, filters
By the component of sun direct irradiation.
(3.3) the true infrared figure background Filtering Processing step of massif
By the use of the massif background heat field model that obtains of emulation as the background of the infrared figure of true massif, due to the mesh in massif
Mark heat radiation meets the mathematical model of conduction of heat, it is assumed that in certain a moment t0Target location (x0,y0,z0) heat radiation curved surface be BT
(x,y,z,t)
BT(x,y,z,t)
=B (x, y, z, t)+T (x, y, z, t) * Rb(x,y,z,t)
+A(x,y,z,t)+δ(x,y,z,t)
It is moment target background body radiation field BT (x, y, z, t), multiple by multi-dielectric body radiation field B (x, y, z, t)
Objective body radiation field T (x, y, z, t) the * R that dielectric has distortedb(x, y, z, t), water body/ground body scatters and disappears with air contact surfaces
Amount of radiation δ (x, y, z, t) and shine upon impact A (x, y, z, t) and produce jointly.
The impact of target background body radiation field is mainly produced by B (x, y, z, t) and A (x, y, z, t), therefore has following public affairs
Formula：
T (x, y, z, t)=BT (x, y, z, t)-k*B (x, y, z, t)-A (x, y, z, t)
Wherein T (x, y, z, t) for target approximate thermal radiation field, BT (x, y, z, t) be target background body radiation field, B (x,
Y, z, t) it is multi-dielectric body background radiation field, A (x, y, z, t) is the energy field for shining upon impact, and k is background radiation field
Adjustability coefficients.Finally obtain the filtered image of the infrared figure background of true massif.
(4) buried target space constraint mean cluster Detection location step
From the filtered image of background, choosing images to be recognized block has s, and template size is 3*3, is respectively b1,b2,
b3,...,bs, there is object region with lower without object region including under, house and road are avoided when selection
Affect.
Utilization space constrains means clustering algorithm road segment segment b1,b2,b3,...,bsHave under being divided into object region with
It is lower without the class of object region two.Space constraint means clustering algorithm to implement process as follows：
Step1：For all sample point bi, computed range ratio
Select ViMinimum point biAs first class heart, juxtaposition q=1；
Step2：To p=1,2, by bi, i=1,2 ..., s are assigned to from its nearest class, and update the class heartI=1,2, NiIt is the sample number of the i-th class；
Step3：Q=q+1 is put, if q>2, algorithm stops；
Step4：The optimal initial central point of next class is selected to makeMinimum point bi, proceed to
Step2。
Result after above formula is clustered, that big class of gray value as doubtful buried target a class, gray scale
A class of the little class of value as non-doubtful buried target.The position of buried target is obtained finally by space constraint clustering algorithm
Put.
Description of the drawings
Fig. 1 is schematic flow sheet of the present invention；
Fig. 2 (a) is massif altitude information schematic diagram；
Fig. 2 (b) is massif equal pitch contour schematic diagram；
Fig. 2 (c) is schematic diagram before massif stress and strain model；
Fig. 2 (d) is schematic diagram after massif stress and strain model；
Fig. 3 is the radiation model schematic diagram of massif；
Fig. 4 is the true infrared image of massif；
Fig. 5 is the background heat field model image after mapping；
Fig. 6 is massif male and female face demarcation line pixel schematic diagram nearby；
Fig. 7 is massif male and female face demarcation line schematic diagram；
Fig. 8 is that massif eliminates the infrared image after sun direct irradiation affects；
Fig. 9 is based on the filtered infrared image of thermal field Model Background；
Figure 10 is underground objects detection result schematic diagram.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, and
It is not used in the restriction present invention.As long as additionally, technical characteristic involved in invention described below each embodiment
Not constituting conflict each other just can be mutually combined.
The flow process of the present invention is as shown in figure 1, wherein specific implementation is comprised the following steps.The present invention includes the massif back of the body
Scape thermal field model establishment step, massif background thermal field 8 are to 16 bit mapping steps, using the massif background heat field model after mapping
Carry out background filter step, buried target space constraint mean cluster Detection location step：
(1) foundation of massif background heat field model, including following sub-step：
(1.1) establishment step of mountain model
(1.1.1) the 3-dimensional digital elevation model for obtaining massif is surveyed and drawn by remote sensing, obtains the height above sea level of true massif
Shown in data message, such as Fig. 2 (a).
(1.1.2) structure of ANSYS geometry mountain model is constituted with point, line, surface and body, and point (coordinate) is to build
The basis of geometric model.So according to above-mentioned altitude information, the establishment of geometry massif is the curve that closure is generated by key point,
The curve of closure generates plane, then surrounds geometry massif by the curved surface for closing.And geometry massif just constitutes whole massif mould
Shown in type, such as Fig. 2 (b) and Fig. 2 (c).
(1.2) the FEM meshing step of mountain model
Anasy FEM meshings are to carry out the vital step of numerical simulation analysis, due to massif model simultaneously
It is not regular, therefore carries out free mesh, is free to automatically generate triangle or tetrahedron net on the whole
Lattice, automatically generate tetrahedral grid on body, while manually carrying out the control of smart dimensions.This example adopts secondary tetrahedron list
First (No. 92 units), it is ensured that computational accuracy is calculated, shown in such as Fig. 2 (d).
(1.3) mountain model boundary condition is arranged and solution procedure
The setting of load boundary condition is carried out to the massif after above-mentioned stress and strain model, using massif conduction of heat and massif-sky
The basic heat transfer physical basis of gas thermal convection current, according to pyroconductivity K of massif and thermal convection current rate Φ of massif-air heat is arranged
The parameter of transmission, through the solution of Ansys the thermo parameters method of massif is calculated, and finally the thermo parameters method of massif is carried out
The mapping of gray-scale maps and the adjustment of temperature resolution, finally give massif radiation model.In this example, K=3.49Kg/m3,
Φ=3W/ (m^2.C), solving result is as shown in Figure 3.
(2) massif background thermal field 8 is to 16 bit mapping processes, including following sub-step：
Because the infrared thermal field model image of massif background set up is 8, and real massif background heat field picture is 16
Position, so needing the mapping that 8 to 16 are carried out to massif background heat field model obtained above and true massif background thermal field
Process.In the case where the thermal field of model is roughly the same with the change of real heterogeneity phantom, it is ensured that model thermal field is more nearly very
The thermal field of real massif.
(2.1) the determination step of the scope of true massif background gray levels
The thermal field of true massif background is affected by many extraneous factors, such as house, road etc..It is extraneous due to these
The shared ratio very little in whole background of factor, therefore interference can be considered as.So needing to carry out range constraint process, lead to
Statistics with histogram is crossed, threshold process is carried out, the interference of extraneous factor is rejected, true massif infrared image is as shown in figure 4, concrete mistake
Journey is as follows：
(2.1.1) pixel grayscale in variable r representative images is set, under discrete situation, r is usedkRepresent discrete gray levels,
With P (rk) probability density function is represented, there is following formula to set up:
K=0,1,2...l-1
N in formulakTo there is r in imagekThe pixel count of gray scale, n is pixel count sum in image,In being exactly theory of probability
Frequency, l is the total number of gray level.
It is P% that known extraneous factor affects to account for occupied area ratio in entire image, then have following formula：
Pr(rk)≥P
Add up grey level histogram successively, if aggregate-value is more than or equal to object proportion, stop cumulative, record rk
Value, as the standard of background.In this example, P=0.5, rk=31000.
(2.2) the map correction step of massif thermal field model
The gray value of massif thermal field background carries out Linear Mapping school according to the minimum gray value of value obtained above and image
Just process.Specific formula is as follows：
Wherein, I for thermal field model gray value, IlFor the minimum brightness gray value of thermal field model, IhFor thermal field model most
High brightness gray value, OlFor the minimum brightness gray value of true massif infrared image, OhFor the above-mentioned r for trying to achievek, O is map correction
Massif background model afterwards.Massif background after correction is as shown in Figure 5.In this example, Ol=29852.
(3) background filter step, including following sub-step are carried out using the massif background heat field model after mapping：
Angle and the impact of solar illumination angle due to remote sensor observation, can be divided into sunny side and the back by massif.The sun is straight
The massif part for connecing irradiation is referred to as sunny side, the sun cannot the part of direct irradiation be referred to as the back.Massif background and surrounding have
Heat radiation when heat radiation, direct irradiation and non-immediate irradiation has differences.Moreover, the thermal field of massif background and night between daytime
Between massif background thermal field it is also different, the present invention is directed massif background heat field model constraint underground thermal source daytime between detect
Positioning.
The pixel of the boundary of the back and sunny side is found first, using the method fitting back and sun of least square fitting
The cut-off rule in face, then carries out grey level compensation operation to the back, finally carries out background Filtering Processing.
(3.1) original infrared image back sunny side demarcation line extraction step
(3.1.1) pixel of boundary is determined using the difference of the back and sunny side gray scale difference value, according to view data
Information can determine that demarcation line is east-west, so more upper and lower pixel gray value is only needed, if neighbouring up and down
Pixel grey scale difference be more than K, formula is as follows：
G(x,y)>G(x,y-1)+K
G(x,y)>G(x,y-2)+K
G(x,y)>G(x,y-3)+K
G(x,y)>G(x,y-1)+K
If aforementioned four inequality is set up, just (x, y) is considered as into the pixel near demarcation line, traversal full figure is divided
All pixels point near boundary line, as shown in Figure 6.In this example, K=40.
(3.1.2) next cubic polynomial least square is carried out to the pixel near all demarcation line obtained above
Method linear process, detailed process is as follows：
WhereinFor the cubic polynomial of least square fitting, err is error target function, is reached by making err minimums
It is fitted to optimum cubic polynomial, obtains final demarcation line, as shown in Figure 7.In this example, a0=0, a1=0.001, a2
=0.4635, a3=42.5124.
(3.2) the step of eliminating sunny side sun direct irradiation energy
After obtaining infrared image back region and sunny side region according to demarcation line, using following mapping policy to infrared
The gray value in image infrared image sunny side region is eliminated：
D=Fn'oshadow(i,j)-Fnoshadow(i,j)
F' in formulanoshadow(i, j) is the sunny side area grayscale value of the non-direct irradiation of the infrared image sun, Fnoshadow(i,j)
It is infrared image sun direct irradiation sunny side area grayscale value, mshadowAnd σshadowIt is the equal of infrared image sunny side area grayscale value
Value and variance, mnoshadowAnd σnoshadowIt is the average and variance of neighbouring non-infrared image infrared image back area grayscale value, A
For compensation intensity coefficient, D is the energy gray value of sun direct irradiation sunny side.In this example, mshadow=6478, mnoshadow=
4478, σshadow=55.2752, σnoshadow=61.9714, D=225, A=1.0.
After the gray value of sun direct irradiation generation is obtained from above-mentioned, all sun for detecting are traveled through
Face region, by infrared image sunny side region the gray value compensation D of each point of infrared figure is deducted, and be eliminated the sun
Gray-scale maps after irradiation impact.The impact for shining upon is not accounted for before, the component for shining upon is filtered, filter the sun
The energy that sunny side is shined upon, shines upon the thermal for causing, and is absorbed by sunny side, under thermal equilibrium condition, filters
By the component of sun direct irradiation, as a result as shown in Figure 8.
(3.3) the true infrared figure background Filtering Processing step of massif
By the use of the massif background heat field model that obtains of emulation as the background of the infrared figure of true massif, due to the mesh in massif
Mark heat radiation meets the mathematical model of conduction of heat, it is assumed that in certain a moment t0Target location (x0,y0,z0) heat radiation curved surface be BT
(x,y,z,t)
BT(x,y,z,t)
=B (x, y, z, t)+T (x, y, z, t) * Rb(x,y,z,t)
+A(x,y,z,t)+δ(x,y,z,t)
It is moment target background body radiation field BT (x, y, z, t), multiple by multi-dielectric body radiation field B (x, y, z, t)
Objective body radiation field T (x, y, z, t) the * R that dielectric has distortedb(x, y, z, t), water body/ground body scatters and disappears with air contact surfaces
Amount of radiation δ (x, y, z, t) and shine upon impact A (x, y, z, t) and produce jointly.
The impact of target background body radiation field is mainly produced by B (x, y, z, t) and A (x, y, z, t), therefore has following public affairs
Formula：
T (x, y, z, t)=BT (x, y, z, t)-k*B (x, y, z, t)-A (x, y, z, t)
Wherein T (x, y, z, t) for target approximate thermal radiation field, BT (x, y, z, t) be target background body radiation field, B (x,
Y, z, t) it is multi-dielectric body background radiation field, A (x, y, z, t) is the energy field for shining upon generation, and k is background radiation field
Adjustability coefficients.The filtered image of the infrared figure background of true massif is finally obtained, as shown in Figure 9.In this example, k=0.8.
(4) buried target space constraint mean cluster Detection location step
From the filtered image of background, choosing images to be recognized block has s, and template size is 3*3, is respectively b1,b2,
b3...bs, there is object region with lower without object region including under, the shadow of house and road is avoided when selection
Ring.
Utilization space constrains means clustering algorithm road segment segment b1,b2,b3,...,bsHave under being divided into object region with
It is lower without the class of object region two.Space constraint means clustering algorithm to implement process as follows：
Step1：For all sample point bi, computed range ratio
Select ViMinimum point biAs first class heart, juxtaposition q=1；
Step2：To p=1,2, by bi, i=1,2 ..., s are assigned to from its nearest class, and update the class heartI=1,2, NiIt is the sample number of the i-th class；
Step3：Q=q+1 is put, if q>2, algorithm stops；
Step4：The optimal initial central point of next class is selected to makeMinimum point bi, proceed to
Step2。
In this example, m1=310, m2=400
Result after above formula is clustered, that big class of gray value as doubtful buried target a class, gray scale
A class of the little class of value as non-doubtful buried target.The position of buried target is obtained finally by space constraint clustering algorithm
Put, as shown in Figure 10.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not to
The present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc. are limited, all should be included
Within protection scope of the present invention.
Claims (10)
1. remote sensing localization method between the underground thermal source daytime that a kind of massif background heat field model is constrained, it is characterised in that described
Method comprises the steps：
(1) foundation of massif background heat field model, including following sub-step：
(1.1) foundation of mountain model；
(1.2) FEM meshing of mountain model；
(1.3) mountain model boundary condition is arranged and solved；
(2) massif background thermal field 8 is to 16 bit mappings, including following sub-step：
(2.1) determination of true massif background gray levels scope；
(2.2) map correction of massif thermal field model；
(3) background filtering, including following sub-step are carried out using the massif background heat field model after mapping：
(3.1) original infrared image back sunny side boundary line drawing；
(3.2) sunny side sun direct irradiation energy is eliminated；
(3.3) the true infrared figure background Filtering Processing of massif；
(4) buried target space constraint mean cluster Detection location.
2. the method for claim 1, it is characterised in that the step (4) specifically includes following sub-step：
From the filtered image of background, choosing images to be recognized block has s, is respectively b1,b2,b3,...,bs, have mesh including under
With lower without object region, utilization space constrains means clustering algorithm road segment segment b in logo image region1,b2,b3,...,bsPoint
There is the object region with lower without the class of object region two under, wherein space constraint means clustering algorithm was implemented
Journey is as follows：
Step1：For all sample point bi, computed range ratio
Select ViMinimum point biAs first class heart, juxtaposition q=1；
Step2：To p=1,2, by bi, i=1,2 ..., s are assigned to from its nearest class, and update the class heartI=1,2, NiIt is the sample number of the i-th class；
Step3：Q=q+1 is put, if q ＞ 2, algorithm stops；
Step4：The optimal initial central point of next class is selected to makeMinimum point bi, proceed to Step2.
3. method as claimed in claim 1 or 2, it is characterised in that the step (1.1) specifically includes following sub-step：
(1.1.1) the 3-dimensional digital elevation model for obtaining massif is surveyed and drawn by remote sensing, obtains the altitude data of true massif
Information；
(1.1.2) structure of ANSYS geometry mountain model is constituted with point, line, surface and body, and point is to build geometric model
Basis, according to above-mentioned altitude information, the establishment of geometry massif is the curve that closure is generated by key point, by the curve life for closing
Into plane, then geometry massif is surrounded by the curved surface for closing, geometry massif just constitutes whole mountain model.
4. method as claimed in claim 1 or 2, it is characterised in that the step (2.1) specifically includes following sub-step：
(2.1.1) pixel grayscale in variable r representative images is set, r is usedkDiscrete gray levels are represented, with P (rk) represent probability density
Function, has following formula to set up:
N in formulakTo there is r in imagekThe pixel count of gray scale, n is pixel count sum in image,It is exactly the frequency in theory of probability,
L is the total number of gray level；
It is P% that known extraneous factor affects to account for occupied area ratio in entire image, then have following formula：
Pr(rk)≥P
Add up grey level histogram successively, if aggregate-value is more than or equal to object proportion, stop cumulative, record rkValue,
As the standard of background.
5. method as claimed in claim 4, it is characterised in that the step (2.2) is specially：
The value and the minimum gray value of image that the gray value of massif thermal field Model Background is obtained according to step (2.1) is linearly reflected
Penetrate correction process；Specific formula is as follows：
Wherein, I for thermal field model gray value, IlFor the minimum brightness gray value of thermal field model, IhFor the most highlighted of thermal field model
Degree gray value, OlFor the minimum brightness gray value of true massif infrared image, OhFor the r that step (2.1) is tried to achievek, O is mapping school
Massif background model after just.
6. method as claimed in claim 1 or 2, it is characterised in that the step (3.1) specifically includes following sub-step：
(3.1.1) pixel of boundary is determined using the difference of the back and sunny side gray scale difference value, according to the information of view data
It is east-west to define boundaries, the upper and lower pixel gray value of comparison, if pixel grey scale difference neighbouring up and down is more than K,
Formula is as follows：
G (x, y) ＞ G (x, y-1)+K
G (x, y) ＞ G (x, y-2)+K
G (x, y) ＞ G (x, y-3)+K
G (x, y) ＞ G (x, y-1)+K
If aforementioned four inequality is set up, (x, y) is considered as into the pixel near demarcation line, traversal full figure obtains demarcation line
Neighbouring all pixels point；
(3.1.2) cubic polynomial least square fitting is carried out to the pixel near all demarcation line obtained above linear
Process, detailed process is as follows：
WhereinFor the cubic polynomial of least square fitting, err is error target function, is optimal by making err minimums
Cubic polynomial fitting, obtain final demarcation line.
7. method as claimed in claim 1 or 2, it is characterised in that the step (3.2) specifically includes following sub-step：
After obtaining infrared image back region and sunny side region according to demarcation line, using following mapping policy to infrared image
The gray value in infrared image sunny side region is eliminated：
D=F 'noshadow(i,j)-Fnoshadow(i,j)
F' in formulanoshadow(i, j) is the sunny side area grayscale value of the non-direct irradiation of the infrared image sun, Fnoshadow(i, j) is red
Outer image sun direct irradiation sunny side area grayscale value, mshadowAnd σshadowBe infrared image sunny side area grayscale value average and
Variance, mnoshadowAnd σnoshadowIt is the average and variance of neighbouring non-infrared image infrared image back area grayscale value, A is benefit
Strength factor is repaid, D is the energy gray value of sun direct irradiation sunny side.
8. method as claimed in claim 1 or 2, it is characterised in that the step (3.3) specifically includes following sub-step：
By the use of the massif background heat field model that obtains of emulation as the background of the infrared figure of true massif, it is assumed that in certain a moment t0Target
Position (x0,y0,z0) heat radiation curved surface be BT (x, y, z, t)
BT(x,y,z,t)
=B (x, y, z, t)+T (x, y, z, t) * Rb(x,y,z,t)
+A(x,y,z,t)+δ(x,y,z,t)
Moment target background body radiation field BT (x, y, z, t), by multi-dielectric body radiation field B (x, y, z, t), by multi-dielectric
Objective body radiation field T (x, y, z, t) the * R that body has distortedbThe lost radiation in (x, y, z, t), water body/ground body and air contact surfaces
Energy field A (x, y, z, t) measured δ (x, y, z, t) and shine upon impact is produced jointly；
The impact of target background body radiation field is produced by B (x, y, z, t) and A (x, y, z, t), there is equation below：
T (x, y, z, t)=BT (x, y, z, t)-k*B (x, y, z, t)-A (x, y, z, t)
Wherein T (x, y, z, t) for target approximate thermal radiation field, BT (x, y, z, t) be target background body radiation field, B (x, y, z,
T) it is multi-dielectric body background radiation field, A (x, y, z, t) is the energy field for shining upon impact, and k is adjustable for background radiation field
Coefficient；Finally obtain the filtered image of the infrared figure background of true massif.
9. method as claimed in claim 1 or 2, it is characterised in that the step (1.2) is specially：
Free mesh is carried out, triangle or tetrahedral grid are freely automatically generated on the whole, given birth to automatically on body
Into tetrahedral grid, while manually carrying out the control of smart dimensions.
10. method as claimed in claim 1 or 2, it is characterised in that the step (1.3) is specially：
The setting of load boundary condition is carried out to the massif after above-mentioned stress and strain model, using massif conduction of heat and massif-air heat
The basic heat transfer physical basis of convection current, according to pyroconductivity K of massif and thermal convection current rate Φ of massif-air heat transfer is arranged
Parameter, be calculated the thermo parameters method of massif through the solution of Ansys, finally gray scale is carried out to the thermo parameters method of massif
The mapping of figure and the adjustment of temperature resolution, finally give massif thermal field model.
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