CN103714344A - Geometrical component and radial gradient angle-based aircraft remote sensing detection method - Google Patents

Abstract

The invention discloses a geometrical component and radial gradient angle-based aircraft remote sensing detection method. The method includes the following steps that: a geometric atomic library is established according to special geometric contours for an aircraft; based on a sparse representation principle, a known aircraft contour is sparsely represented as a combination of geometric atoms; an aircraft star-type component model is constructed with the geometric atoms adopted as components; a sliding window method is adopted to scan a test image; the principal direction of each image block is estimated through a radial-gradient angle voting algorithm, such that the direction of the component model can be adjusted; a total similarity distribution diagram of each position is calculated through using the adjusted model; and whether the aircraft exists in a detected area is judged through maximum posteriori probability reasoning, and the position of the aircraft can be further obtained. According to the geometrical component and radial gradient angle-based aircraft remote sensing detection method, the sparse representation principle is utilized to adaptively select the geometric components, and the principal directions are estimated through the radial-gradient angle voting algorithm which adopts the contours as guidance, and therefore, aircraft remote sensing detection at different directions in a complex background can be realized. The geometrical component and radial gradient angle-based aircraft remote sensing detection method is advantageous in simple algorithm, fast detection speed, better robust and more accurate detection results.

Description

Remote sensing based on geometry parts and radial gradient angle detects the method for aircraft

Technical field

The present invention relates to a kind of method that remote sensing detects aircraft.

Background technology

The sixties in 20th century, it is sign that the USSR (Union of Soviet Socialist Republics) of take has been launched its first man made earth satellite in the world, and " remote sensing " starts to enter the mankind's work and life.Remote sensing refers to from the platform away from ground, adopts sensor to survey ground, by collecting, analyzing and process, surveys the signal obtaining, and obtains the relevant information on ground.Along with the develop rapidly of computing machine and sensor devices, remote sensing technology is widely applied, and relates to environmental disaster control, city planning, and a plurality of fields such as resource exploration, produce far-reaching influence to national defense construction and human lives.

Aircraft, as common means of transport, carries out remote sensing to it and detects transport power planning, scheduling controlling and the emergency processing (as searching of wrecked aircraft) etc. that are conducive to improve air transportation.Due to computer technology and sensor devices develop rapidly, the resolution of remotely sensed image can reach panchromatic wave-band 0.61m, and the spatial resolution of colored wave band 2.44m can adopt finer aircraft local feature to describe aircraft.The people such as Zhang are at document 1(Wanceng Zhang, Xian Sun.Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model.IEEE Geoscience and Remote Sensing Letters.2013. http:// ieeexplore.ieee.org/xpls/absall.jsp arnumber=6512596 & tag=1) in, propose the partial model based on invariable rotary histogram of gradients RIHOG, realize the remote sensing images Airplane detection of different directions.The method first people calibrates the parts that training image is concentrated each correcting image; Then to each parts, adopt gradient orientation histogram HOG method statistic to obtain its principal direction, and carry out rotation correction; Finally by the wave filter of SVM method training production part.Each parts that it is concentrated training image need artificial demarcation, lack dirigibility, and demarcate training plan image set and produce huge workload, while increasing modeling, consume; In addition, parts wave filter and principal direction are estimated all to take picture material as guidance, make to detect the easy impact because of color, texture, illumination and complex background and lead to the failure.Summary of the invention

The object of the present invention is to provide a kind of remote sensing based on geometry parts and radial gradient angle to detect the method for aircraft, the method can detect the aircraft of different directions under complex background automatically, and it detects fast, and result is accurate.

The present invention solves its technical matters, and the technical scheme adopting is: the remote sensing based on geometry parts and radial gradient angle detects the method for aircraft, comprises the following steps:

The foundation of A, the star-like partial model of aircraft:

A1, by the known aircraft image rectification in known remote sensing images be head straight up, obtain correcting image;

A2, the pixel value of aircraft part in correcting image is set to 1, background parts pixel value is set to 0, obtains the two-value profile P of known aircraft _b;

A3, the two-value profile P to known aircraft _bcarry out Its Sparse Decomposition, obtain optimum atomic diagram image set

n ^optfor optimum atomic diagram is as number, i is optimum atomic diagram picture

index number,

be i optimum atomic diagram picture;

A4, get i optimum atomic diagram picture

in non-zero region obtain i geometric figure

with i geometric figure

as i parts Part _i, with the two-value profile P of known aircraft _bfor root part Root, carry out Star Model association; When associated, i parts Part _ibe shaped as i geometric figure

shape i parts Part _ito root part Root center (x ^r, y ^r) relative coordinate position be i geometric figure center is to atomic diagram picture

the relative coordinate at center

thereby i parts Part _iprincipal direction be i geometric figure principal direction

wherein, x ^r, y ^rbe respectively horizontal ordinate and the ordinate of root Root center in correcting image;

A5, repetition A4, by all geometric figures

all as parts Part _icarry out associatedly with root part Root, set up and to obtain the star-like partial model of aircraft;

Airplane detection in B, remote sensing images

B1, remote sensing via satellite obtain the remote sensing images P of surveyed area, get the two-value profile P of size and known aircraft _bidentical detection window carries out point by point scanning in remote sensing images P, obtains subimage block P _sub;

B2, radially-gradient angle Voting Algorithm of employing calculate subimage block P _subprincipal direction ξ _sub;

B3, aircraft Star Model is rotated to heading and subimage block P _subprincipal direction ξ _subunanimously, calculate each parts Part in postrotational aircraft Star Model _iprincipal direction

and with root part Root center (x ^r, y ^r) relative coordinate

$\left(\begin{array}{c}{\mathrm{Px}}_R^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}_R^{{\mathrm{Part}}_i}\end{array}\right)=\left(\begin{array}{cc}\cos {\mathrm{\ξ}}_{\mathrm{sub}}& \sin {\mathrm{\ξ}}_{\mathrm{sub}}\\ -\sin {\mathrm{\ξ}}_{\mathrm{sub}}& \cos {\mathrm{\ξ}}_{\mathrm{sub}}\end{array}\right)\left(\begin{array}{c}{\mathrm{Px}}^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}^{{\mathrm{Part}}_i}\end{array}\right)$

B4, in aircraft Star Model according to i parts Part _ipostrotational principal direction

i parts Part _irotate rear and root part Root center relative coordinate

and the shape of i parts

adopt method of geometry to calculate rear i i the region that parts cover of aircraft Star Model rotation

B5, by i region in aircraft Star Model

interior pixel is set to 1, and other pixel is set to 0, generates i two-value component diagram

that is:

${\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)=\left\{\begin{array}{c}1,(x.y)\∈{\mathrm{Reg}}_R^{{\mathrm{Part}}_i}\\ 0,\mathrm{otherwise}\end{array}\right.$

In formula, x, y is respectively two-value component diagram

horizontal ordinate and ordinate;

B6, i two-value component diagram of calculating

histograms of oriented gradients, generate i parts wave filter F _i:

$F_i=\mathrm{HOG}\left({\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)\right)$

In formula, HOG () is histograms of oriented gradients feature extraction operator;

The subimage block P of B6, calculating remote sensing images P _subhistograms of oriented gradients H _sub:

H _sub=HOG(P _sub)

B7, calculating subimage block P _subtotal similarity Score with all parts in postrotational aircraft Star Model:

In formula, < > is inner product operator;

B8, repeat B1～B7, calculate total similarity Score of all detection windows in remote sensing images P, obtain total similarity Score distribution plan S;

C, in total similarity Score distribution plan S, have the total similarity Score that is greater than setting threshold, judge in surveyed area and have aircraft, its corresponding detection window position in remote sensing images is aircraft location.

Compared with prior art, the invention has the beneficial effects as follows:

One, the automatic geometry atomic diagram picture producing of method that the present invention passes through rarefaction representation using the profile of known remote sensing images aircraft is as parts, set up the star-like partial model of aircraft, compare with traditional Star Model, the resulting component representation performance of the inventive method is stronger, not affected by the factors such as color, texture, illumination; Good stability, the testing result of parts are accurate;

Two, the present invention estimates the subimage block principal direction obtaining according to radially-gradient Voting Algorithm, adjusts the direction of the star-like partial model of aircraft, and adopts the parts wave filter after adjusting to detect, thereby realizes the Airplane detection of different directions.

In a word, the inventive method chooses by the method self-adaptation of the profile rarefaction representation of known remote sensing images aircraft how much parts that modeling of aircraft is used, and makes parts have better robustness; By the star-like partial model direction rotation of aircraft, the principal direction to subimage block detects again simultaneously, can realize the Airplane detection of different directions.

In above-mentioned steps A2 to known aircraft two-value profile P _bthe specific practice of carrying out Its Sparse Decomposition is:

1) generate former word bank D how much

A, construct oval former word bank D _e:

Define oval atomic diagram as g _e(x, y) is as follows:

In formula, x, y is respectively that oval atomic diagram is as g _e(x, y) horizontal ordinate and ordinate in correcting image, oval atomic diagram is as g _ethe two-value profile P of the size of (x, y) and known aircraft _bit is identical,

for oval atomic diagram is as g _eelliptic region in (x, y), by following formula, calculated:

In formula, u, v be respectively horizontal ordinate x and ordinate y with oval atomic diagram as g _e(x, y) center is that the point of rotation turns clockwise

after new horizontal ordinate and ordinate, M _pBand N _pBbe respectively the two-value profile P of known aircraft _blength and width; (px, py) is elliptic region

center is to the two-value profile P of known aircraft _bthe relative coordinate at center, px ∈ [M _pB/ 2, M _pB/ 2], py ∈ [N _pB/ 2, N _pB/ 2]; A and b are respectively elliptic region

major axis and minor axis,

for elliptic region

principal direction,

By parameter a, b,

px, py gets the oval atomic diagram that generates all over above all values as g _e(x, y), forms oval former word bank D _e:

B, construct trapezoidal former word bank D _t:

Define trapezoidal atomic diagram as g _t(x, y) is as follows:

In formula, x, y is respectively that trapezoidal atomic diagram is as g _t(x, y) horizontal ordinate and ordinate in correcting image, trapezoidal atomic diagram is as g _tthe two-value profile P of the size of (x, y) and known aircraft _bidentical;

for trapezoidal atomic diagram is as g _ttrapezoid area in (x, y), is surrounded by four straight lines, by following formula, is calculated:

In formula, u, v is respectively that horizontal ordinate x and ordinate y justify atomic diagram as g with ladder _t(x, y) center is that the point of rotation turns clockwise

after horizontal ordinate and ordinate, ub, lb and h are respectively trapezoidal upper base, go to the bottom and high,

f is upper base center and the side-play amount at the center of going to the bottom, regulation upper base center toward left avertence for negative, toward right avertence for just, f ∈ [50,50];

with px, the definition of py and span and oval atomic diagram are as g _e(x, y) is identical;

By parameters u b, lb, h, f, px, py gets the trapezoidal atomic diagram that generates all over all values as g _t(x, y), forms trapezoidal former word bank D _t:

C, by the former word bank D of all ellipses _ein atomic diagram as g _e(x, y) and trapezoidal former word bank D _tatom image g _t(x, y) merges, and generates the two-value profile P of known aircraft _bthe former word bank D of geometry:

D=D _e∪D _t

D, by all atomic diagram in how much former word bank D as g _e(x, y), g _t(x, y) renames numbering, is expressed as g _j(x, y), j=1,2 ..., N, N be atomic diagram in how much former word bank D as number, j renames atomic diagram after numbering as g _jthe index number of (x, y);

2) set up the optimal model of aircraft profile rarefaction representation:

min||Ω|| ₀

$s.t.{\left|\right|P_B-\underset{i\&Element;\mathrm{\&Omega;}}{\mathrm{\&Sigma;}}{g}_i\left|\right|}_2<\mathrm{\&sigma;}$

<g _i,g _j>=0,i≠j

In formula, || || ₀for 0-norm, the element number of expression set, || || ₂for 2-norm, represent that energy, < > are that indicate satisfied condition, σ of inner product operator, s.t. is energy threshold, get the two-value profile P of known aircraft _b15% of area, i.e. σ=15% * Area (P _b), Area () is that area operator, Ω are the subspace in the former word bank D of geometry satisfying condition,

3) adopt quantum genetic algorithm to solve above optimal model, obtain optimum atom image combining

be i optimum atomic diagram picture, N ^optfor optimum atomic diagram picture

number, i be optimum atomic diagram picture

index number.

Like this, the parts in model can be chosen adaptively according to the profile feature of different aircrafts, and are applicable to various aircraft, and its applicability is strong, and dirigibility is good; The part count that self-adaptation obtains is less, makes its detection speed faster.

Employing in above-mentioned steps B2 radially-gradient Voting Algorithm calculates subimage block P _subprincipal direction θ _subspecific practice be:

A, extraction subimage block P _subtwo-value profile P with known aircraft _bhistograms of oriented gradients feature H _sub(m, n, γ) and H _b(m, n, γ):

H _sub(m,n,γ)=HOG(P _sub)

H _B(m,n,γ)=HOG(P _B)

In formula, m, n, γ is respectively the horizontal ordinate in histograms of oriented gradients, ordinate and gradient direction;

B, be calculated as follows subimage block P _subtwo-value profile P with known aircraft _bmain gradient direction figure

with

$H_{\mathrm{sub}}^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_{\mathrm{sub}}(m,n,\mathrm{\&gamma;})$

$H_B^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_B(m,n,\mathrm{\&gamma;})$

C, by subimage block P _submain gradient direction figure

two-value profile P with known aircraft _bmain gradient direction figure

be converted to polar form, obtain subimage block P _subdirection gradient polar plot PH _subthe two-value profile P of (r, θ) and known aircraft _bdirection gradient polar plot PH _b(r, θ), wherein, r, θ is respectively utmost point footpath coordinate and the polar angle coordinate of direction gradient polar plot;

D, press following formula antithetical phrase image block P _subdirection gradient polar plot PH _sub(r, θ) screens, and obtains candidate direction gradient-pole coordinate diagram C (r, θ):

$C(r,\mathrm{\&theta;})=\left\{\begin{array}{cc}{\mathrm{PH}}_{\mathrm{sub}}(r,\mathrm{\&theta;}),& \underset{\mathrm{\&phi;}\&Element;\mathrm{\&Phi;}}{\inf }\left(\right|{\mathrm{RGA}}_{\mathrm{sub}}(r,\mathrm{\&theta;})-{\mathrm{RGA}}_B(r,\mathrm{\&phi;})\left|\right)<\mathrm{\&epsiv;}\\ -10,& \mathrm{otherwise}\end{array}\right.$

In formula, inf () is that infimum operator, ε are threshold value, get 0.1-0.2, Φ=θ | PH _b(r, θ) ≠-10} is the two-value profile P of known aircraft _bdirection gradient polar plot PH _bin (r, θ), with subimage block P _subdirection gradient polar plot PH _sub(r, θ) has same pole footpath r, and gradient direction value is not-10 polar angle collection, RGA _sub(r, θ) and RGA _b(r, θ) is respectively subimage block P _subtwo-value profile P with known aircraft _bradially-gradient angle, be calculated as follows:

RGA _sub(r,θ)=|PH _sub(r,θ)-θ|

RGA _B(r,θ)=|PH _B(r,θ)-θ|

E, make gradient direction in candidate direction gradient-pole coordinate diagram C (r, θ) for-10 point is available point, and be calculated as follows out the prediction rotation angle R (k) of available point:

$R\left(k\right)={\mathrm{\&theta;}}_k^{\mathrm{sub}}-{\mathrm{\&theta;}}_k^B,k=\mathrm{1,2},...,N_C$

In formula, the index number that k is available point, N _cfor the number of available point,

be k the corresponding polar angle of available point, two-value profile P for known aircraft _bdirection gradient polar plot PH _bpoint in (r, θ) in radially-gradient angle and candidate direction gradient-pole coordinate diagram

the corresponding polar angle of immediate point, wherein

for candidate direction gradient-pole coordinate diagram mid point

radially-gradient angle;

F, by following formula to the statistics of voting of the rotation angle in the prediction rotation angle R (k) of available point, obtain the histogram His of rotation angle _r:

His _R(λ)=#{k|R(k)=λ}

In formula, # () represents the number of set.

His _rthe direction of middle votes maximum is subimage block P _subprincipal direction ξ _sub, that is:

By above-mentioned voting process, the calculating of principal direction is not subject to the impact of the factors such as color, texture, illumination, can under complex background, obtain precise and stable principal direction; Its computing method speed is fast, than the computation complexity of the method for exhaustion, greatly reduces.

Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.

Accompanying drawing explanation

Fig. 1 a is the correcting image of embodiment emulation experiment 1-3; Fig. 1 b is the two-value profile P of the known aircraft of embodiment emulation experiment 1-3 _b; Fig. 1 c is the optimum atomic diagram image set Ω of embodiment emulation experiment 1-3 _opt(number in the figure is 1,2 ..., 5 image block is respectively atomic diagram picture

Fig. 2 is the star-like partial model of aircraft of embodiment of the present invention emulation experiment 1-3, wherein Part _i(i=1,2,3,4,5) represent the two-value profile P of known aircraft _b5 how much parts, F _i(i=1,2,3,4,5) represent corresponding parts wave filter.

Fig. 3 a is the remote sensing images P of the surveyed area of emulation experiment 1; Fig. 3 b is total similarity Score distribution plan S that emulation experiment 1 adopts document 1 algorithm to obtain; Fig. 3 c is total similarity Score distribution plan S that emulation experiment 1 adopts the inventive method to obtain.

Fig. 4 a is the remote sensing images P of the surveyed area of emulation experiment 2; Fig. 4 b is total similarity Score distribution plan S that emulation experiment 2 adopts document 1 algorithm to obtain; Fig. 4 c is total similarity Score distribution plan S that emulation experiment 2 adopts the inventive method to obtain.

Fig. 5 a is the remote sensing images P of the surveyed area of emulation experiment 3; Fig. 5 b is total similarity Score distribution plan S that emulation experiment 3 adopts document 1 algorithm to obtain; Fig. 5 c is total similarity Score distribution plan S that emulation experiment 3 adopts the inventive method to obtain.

Embodiment

A kind of embodiment of the present invention is that the remote sensing based on geometry parts and radial gradient angle detects the method for aircraft, comprises the following steps:

The foundation of A, the star-like partial model of aircraft:

A1, by the known aircraft image rectification in known remote sensing images be head straight up, obtain correcting image;

A2, the pixel value of aircraft part in correcting image is set to 1, background parts pixel value is set to 0, obtains the two-value profile P of known aircraft _b;

A3, the two-value profile P to known aircraft _bcarry out Its Sparse Decomposition, obtain optimum atomic diagram image set

n ^optfor optimum atomic diagram is as number, i is index number, be i optimum atomic diagram picture; Concrete steps are as follows:

The described remote sensing based on geometry parts and radial gradient angle detects the method for aircraft, it is characterized in that the two-value profile P to known aircraft in described steps A 2 _bthe specific practice of carrying out Its Sparse Decomposition is:

1) generate former word bank D how much

A, construct oval former word bank D _e:

Define oval atomic diagram as g _e(x, y) is as follows:

In formula, x, y is respectively that oval atomic diagram is as g _e(x, y) horizontal ordinate and ordinate in correcting image, oval atomic diagram is as g _ethe two-value profile P of the size of (x, y) and known aircraft _bit is identical,

for oval atomic diagram is as g _eelliptic region in (x, y), by following formula, calculated:

In formula, u, v be respectively horizontal ordinate x and ordinate y with oval atomic diagram as g _e(x, y) center is that the point of rotation turns clockwise

after new horizontal ordinate and ordinate, M _pBand N _pBbe respectively the two-value profile P of known aircraft _blength and width; (px, py) is elliptic region

center is to the two-value profile P of known aircraft _bthe relative coordinate at center, px ∈ [M _pB/ 2, M _pB/ 2], py ∈ [N _pB/ 2, N _pB/ 2]; A and b are respectively elliptic region

major axis and minor axis,

for elliptic region

principal direction,

By parameter a, b,

px, py gets the oval atomic diagram that generates all over above all values as g _e(x, y), forms oval former word bank D _e:

B, construct trapezoidal former word bank D _t:

Define trapezoidal atomic diagram as g _t(x, y) is as follows:

In formula, x, y is respectively that trapezoidal atomic diagram is as g _t(x, y) horizontal ordinate and ordinate in correcting image, trapezoidal atomic diagram is as g _tthe two-value profile P of the size of (x, y) and known aircraft _bidentical;

for trapezoidal atomic diagram is as g _ttrapezoid area in (x, y), is surrounded by four straight lines, by following formula, is calculated:

In formula, u, v is respectively that horizontal ordinate x and ordinate y justify atomic diagram as g with ladder _t(x, y) center is that the point of rotation turns clockwise

after horizontal ordinate and ordinate, ub, lb and h are respectively trapezoidal upper base, go to the bottom and high,

f is upper base center and the side-play amount at the center of going to the bottom, regulation upper base center toward left avertence for negative, toward right avertence for just, f ∈ [50,50]; with px, the definition of py and span and oval atomic diagram are as g _e(x, y) is identical;

By parameters u b, lb, h, f,

px, py gets the trapezoidal atomic diagram that generates all over all values as g _t(x, y), forms trapezoidal former word bank D _t:

C, by the former word bank D of all ellipses _ein atomic diagram as g _e(x, y) and trapezoidal former word bank D _tatom image g _t(x, y) merges, and generates the two-value profile P of known aircraft _bthe former word bank D of geometry:

D=D _e∪D _t

D, by all atomic diagram in how much former word bank D as g _e(x, y), g _t(x, y) renames numbering, is expressed as g _j(x, y), j=1,2 ..., N, N be atomic diagram in how much former word bank D as number, j renames atomic diagram after numbering as g _jthe index number of (x, y);

2) set up the optimal model of aircraft profile rarefaction representation:

min||Ω|| ₀

$s.t.{\left|\right|P_B-\underset{i\&Element;\mathrm{\&Omega;}}{\mathrm{\&Sigma;}}{g}_i\left|\right|}_2<\mathrm{\&sigma;}$

<g _i,g _j>=0,i≠j

In formula, || || ₀for 0-norm, the element number of expression set, || || ₂for 2-norm, represent that energy, < > are that indicate satisfied condition, σ of inner product operator, s.t. is energy threshold, get the two-value profile P of known aircraft _b15% of area, i.e. σ=15% * Area (P _b), Area () is that area operator, Ω are the subspace in the former word bank D of geometry satisfying condition,

3) adopt quantum genetic algorithm to solve above optimal model, obtain optimum atom image combining

be i optimum atomic diagram picture, N ^optfor optimum atomic diagram picture

number, i be optimum atomic diagram picture

index number.

A4, get i optimum atomic diagram picture

in non-zero region obtain i geometric figure

with i geometric figure as i parts Part _i, with the two-value profile P of known aircraft _bfor root part Root, carry out Star Model association; When associated, i parts Part _ibe shaped as i geometric figure

shape

i parts Part _ito root part Root center (x ^r, y ^r) relative coordinate position be i geometric figure

center is to atomic diagram picture

the relative coordinate at center

thereby i parts Part _iprincipal direction be i geometric figure

principal direction wherein, x ^r, y ^rbe respectively horizontal ordinate and the ordinate of root Root center in correcting image;

A5, repetition A4, by all geometric figures

all as parts Part _icarry out associatedly with root part Root, set up and to obtain the star-like partial model of aircraft;

Airplane detection in B, remote sensing images

B1, remote sensing via satellite obtain the remote sensing images P of surveyed area, get the two-value profile P of size and known aircraft _bidentical detection window carries out point by point scanning in remote sensing images P, obtains subimage block P _sub;

B2, radially-gradient angle Voting Algorithm of employing calculate subimage block P _subprincipal direction ξ _sub; Concrete steps are as follows:

A, extraction subimage block P _subtwo-value profile P with known aircraft _bhistograms of oriented gradients feature H _sub(m, n, γ) and H _b(m, n, γ):

H _sub(m,n,γ)=HOG(P _sub)

H _B(m,n,γ)=HOG(P _B)

In formula, m, n, γ is respectively the horizontal ordinate in histograms of oriented gradients, ordinate and gradient direction;

B, be calculated as follows subimage block P _subtwo-value profile P with known aircraft _bmain gradient direction figure with

$H_{\mathrm{sub}}^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_{\mathrm{sub}}(m,n,\mathrm{\&gamma;})$

$H_B^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_B(m,n,\mathrm{\&gamma;})$

C, by subimage block P _submain gradient direction figure

two-value profile P with known aircraft _bmain gradient direction figure be converted to polar form, obtain subimage block P _subdirection gradient polar plot PH _subthe two-value profile P of (r, θ) and known aircraft _bdirection gradient polar plot PH _b(r, θ), wherein, r, θ is respectively utmost point footpath coordinate and the polar angle coordinate of direction gradient polar plot;

D, press following formula antithetical phrase image block P _subdirection gradient polar plot PH _sub(r, θ) screens, and obtains candidate direction gradient-pole coordinate diagram C (r, θ):

$C(r,\mathrm{\&theta;})=\left\{\begin{array}{cc}{\mathrm{PH}}_{\mathrm{sub}}(r,\mathrm{\&theta;}),& \underset{\mathrm{\&phi;}\&Element;\mathrm{\&Phi;}}{\inf }\left(\right|{\mathrm{RGA}}_{\mathrm{sub}}(r,\mathrm{\&theta;})-{\mathrm{RGA}}_B(r,\mathrm{\&phi;})\left|\right)<\mathrm{\&epsiv;}\\ -10,& \mathrm{otherwise}\end{array}\right.$

In formula, inf () is that infimum operator, ε are threshold value, get 0.1-0.2, Φ=θ | PH _b(r, θ) ≠-10} is the two-value profile P of known aircraft _bdirection gradient polar plot PH _bin (r, θ), with subimage block P _subdirection gradient polar plot PH _sub(r, θ) has same pole footpath r, and gradient direction value is not-10 polar angle collection, RGA _sub(r, θ) and RGA _b(r, θ) is respectively subimage block P _subtwo-value profile P with known aircraft _bradially-gradient angle, be calculated as follows:

RGA _sub(r,θ)=|PH _sub(r,θ)-θ|

RGA _B(r,θ)=|PH _B(r,θ)-θ|

E, make gradient direction in candidate direction gradient-pole coordinate diagram C (r, θ) for-10 point is available point, and be calculated as follows out the prediction rotation angle R (k) of available point:

$R\left(k\right)={\mathrm{\&theta;}}_k^{\mathrm{sub}}-{\mathrm{\&theta;}}_k^B,k=\mathrm{1,2},...,N_C$

In formula, the index number that k is available point, N _cfor the number of available point,

be k the corresponding polar angle of available point,

two-value profile P for known aircraft _bdirection gradient polar plot PH _bpoint in (r, θ) in radially-gradient angle and candidate direction gradient-pole coordinate diagram

the corresponding polar angle of immediate point, wherein

for candidate direction gradient-pole coordinate diagram mid point radially-gradient angle;

F, by following formula to the statistics of voting of the rotation angle in the prediction rotation angle R (k) of available point, obtain the histogram His of rotation angle _r:

His _R(λ)=#{k|R(k)=λ}

In formula, # () represents the number of set.

His _rthe direction of middle votes maximum is subimage block P _subprincipal direction ξ _sub, that is:

B3, aircraft Star Model is rotated to heading and subimage block P _subprincipal direction ξ _subunanimously, calculate each parts Part in postrotational aircraft Star Model _iprincipal direction

and with root part Root center (x ^r, y ^r) relative coordinate

$\left(\begin{array}{c}{\mathrm{Px}}_R^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}_R^{{\mathrm{Part}}_i}\end{array}\right)=\left(\begin{array}{cc}\cos {\mathrm{\&xi;}}_{\mathrm{sub}}& \sin {\mathrm{\&xi;}}_{\mathrm{sub}}\\ -\sin {\mathrm{\&xi;}}_{\mathrm{sub}}& \cos {\mathrm{\&xi;}}_{\mathrm{sub}}\end{array}\right)\left(\begin{array}{c}{\mathrm{Px}}^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}^{{\mathrm{Part}}_i}\end{array}\right)$

B4, in aircraft Star Model according to i parts Part _ipostrotational principal direction

i parts Part _irotate rear and root part Root center relative coordinate

and the shape of i parts adopt method of geometry to calculate rear i i the region that parts cover of aircraft Star Model rotation

B5, by i region in aircraft Star Model

interior pixel is set to 1, and other pixel is set to 0, generates i two-value component diagram that is:

${\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)=\left\{\begin{array}{c}1,(x.y)\&Element;{\mathrm{Reg}}_R^{{\mathrm{Part}}_i}\\ 0,\mathrm{otherwise}\end{array}\right.$

In formula, x, y is respectively two-value component diagram

horizontal ordinate and ordinate;

B6, i two-value component diagram of calculating

histograms of oriented gradients, generate i parts wave filter F _i:

$F_i=\mathrm{HOG}\left({\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)\right)$

In formula, HOG () is histograms of oriented gradients feature extraction operator;

The subimage block P of B6, calculating remote sensing images P _subhistograms of oriented gradients H _sub:

H _sub=HOG(P _sub)

B7, calculating subimage block P _subtotal similarity Score with all parts in postrotational aircraft Star Model:

In formula, < > is inner product operator;

B8, repeat B1～B7, calculate total similarity Score of all detection windows in remote sensing images P, obtain total similarity Score distribution plan S;

C, in total similarity Score distribution plan S, have the total similarity Score that is greater than setting threshold, judge in surveyed area and have aircraft, its corresponding detection window position in remote sensing images is aircraft location.

Simulation result

Below by emulation experiment, the inventive method and existing document 1 detection method are verified:

Fig. 1 a is the correcting image of emulation experiment 1-3; Fig. 1 b is the two-value profile P of the known aircraft that in emulation experiment 1-3, embodiment method obtains _b; Fig. 1 c is the optimum atomic diagram image set Ω that in emulation experiment 1-3, embodiment method obtains _opt(number in the figure is 1,2 ..., 5 image block is respectively atomic diagram picture

Fig. 2 is the star-like partial model of aircraft that in emulation experiment 1-3, embodiment obtains, wherein Part _i(i=1,2,3,4,5) represent the two-value profile P of known aircraft _b5 how much parts, F _i(i=1,2,3,4,5) represent corresponding parts wave filter.

Fig. 3 a is the remote sensing images P of the surveyed area of emulation experiment 1; Fig. 3 b is total similarity Score distribution plan S that the remote sensing images of Fig. 3 a adopt document 1 algorithm to obtain; Fig. 3 c is total similarity Score distribution plan S that the remote sensing images of Fig. 3 a adopt embodiment of the present invention method to obtain; Aircraft center in Fig. 3 b and Fig. 3 c in the remote sensing images P of white point positional representation surveyed area.

Fig. 4 a is the remote sensing images P of the surveyed area of emulation experiment 2; Fig. 4 b is total similarity Score distribution plan S that the remote sensing images of Fig. 4 a adopt document 1 algorithm to obtain; Fig. 4 c is total similarity Score distribution plan S that the remote sensing images of Fig. 4 a adopt embodiment of the present invention method to obtain; Aircraft center in Fig. 4 b and Fig. 4 c in the remote sensing images P of white point positional representation surveyed area.

Fig. 5 a is the remote sensing images P of the surveyed area of emulation experiment 3; Fig. 5 b is total similarity Score distribution plan S that the remote sensing images of Fig. 5 a adopt document 1 algorithm to obtain; Fig. 5 c is total similarity Score distribution plan S that the remote sensing images of Fig. 5 a adopt embodiment of the present invention method to obtain; Aircraft center in Fig. 5 b and Fig. 5 c in the remote sensing images P of white point positional representation surveyed area.

From above emulation experiment figure, can find out, for the simple Fig. 3 a of background and objective contour Fig. 4 a clearly, adopt document 1 and the inventive method all can obtain accurate testing result; But poor to picture quality, Fig. 5 a that background is more complicated, the testing result that the inventive method obtains is accurate compared with document 1, this is because document 1 adopts gradient orientation histogram feature as parts wave filter, and principal direction distributes to estimate by the direction in statistical gradient direction histogram, because wave filter and principal direction are estimated all to take picture material as guidance, affected by complex background; And how much parts that the inventive method adopts can be resisted the impact of the factors such as color, texture, illumination, the method that principal direction guides by profile is estimated, is conducive to reduce the impact of background on statistics.

Above the simulation experiment result shows, the inventive method can realize the Airplane detection of different directions under complex background, and its positioning precision is high than existing methods, at Remote Sensing Target detection field, has certain feasibility and applicability.

Claims (3)

1. the remote sensing based on geometry parts and radial gradient angle detects a method for aircraft, and its step is as follows:

The foundation of A, the star-like partial model of aircraft:

A1, by the known aircraft image rectification in known remote sensing images be head straight up, obtain correcting image;

A2, the pixel value of aircraft part in correcting image is set to 1, background parts pixel value is set to 0, obtains the two-value profile P of known aircraft _b;

A3, the two-value profile P to known aircraft _bcarry out Its Sparse Decomposition, obtain optimum atomic diagram image set

for optimum atomic diagram is as number, i is optimum atomic diagram picture

index number,

be i optimum atomic diagram picture;

A4, get i optimum atomic diagram picture in non-zero region obtain i geometric figure

with i geometric figure

as i parts Part _i, with the two-value profile P of known aircraft _bfor root part Root, carry out Star Model association; When associated, i parts Part _ibe shaped as i geometric figure

shape

i parts Part _ito root part Root center (x ^r, y ^r) relative coordinate position be i geometric figure

center is to atomic diagram picture the relative coordinate at center

thereby i parts Part _iprincipal direction be i geometric figure

principal direction

wherein, x ^r, y ^rbe respectively horizontal ordinate and the ordinate of root part Root center in correcting image;

A5, repetition A4, by all geometric figures

all as parts Part _icarry out associatedly with root part Root, set up and to obtain the star-like partial model of aircraft;

Airplane detection in B, remote sensing images

B1, remote sensing via satellite obtain the remote sensing images P of surveyed area, get the two-value profile P of size and known aircraft _bidentical detection window carries out point by point scanning in remote sensing images P, obtains subimage block P _sub;

B2, radially-gradient angle Voting Algorithm of employing calculate subimage block P _subprincipal direction ξ _sub;

B3, aircraft Star Model is rotated to heading and subimage block P _subprincipal direction ξ _subunanimously, calculate each parts Part in postrotational aircraft Star Model _iprincipal direction and with root part Root center (x ^r, y ^r) relative coordinate

$\left(\begin{array}{c}{\mathrm{Px}}_R^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}_R^{{\mathrm{Part}}_i}\end{array}\right)=\left(\begin{array}{cc}\cos {\mathrm{\&xi;}}_{\mathrm{sub}}& \sin {\mathrm{\&xi;}}_{\mathrm{sub}}\\ -\sin {\mathrm{\&xi;}}_{\mathrm{sub}}& \cos {\mathrm{\&xi;}}_{\mathrm{sub}}\end{array}\right)\left(\begin{array}{c}{\mathrm{Px}}^{{\mathrm{Part}}_i}\\ {\mathrm{Py}}^{{\mathrm{Part}}_i}\end{array}\right)$

B4, in aircraft Star Model according to i parts Part _ipostrotational principal direction

i parts Part _irotate rear and root part Root center (x ^r, y ^r) relative coordinate

and the shape of i parts

adopt method of geometry to calculate rear i i the region that parts cover of aircraft Star Model rotation

B5, by i region in aircraft Star Model interior pixel is set to 1, and other pixel is set to 0, generates i two-value component diagram

that is:

${\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)=\left\{\begin{array}{c}1,(x.y)\&Element;{\mathrm{Reg}}_R^{{\mathrm{Part}}_i}\\ 0,\mathrm{otherwise}\end{array}\right.$

In formula, x, y is respectively two-value component diagram

horizontal ordinate in correcting image and ordinate;

B6, i two-value component diagram of calculating

histograms of oriented gradients, generate i parts wave filter F _i:

$F_i=\mathrm{HOG}\left({\mathrm{Pic}}^{{\mathrm{Part}}_i}(x,y)\right)$

In formula, HOG () is histograms of oriented gradients feature extraction operator;

The subimage block P of B6, calculating remote sensing images P _subhistograms of oriented gradients H _sub:

H _sub=HOG(P _sub)

B7, calculating subimage block P _subtotal similarity Score with all parts in postrotational aircraft Star Model:

In formula, < > is inner product operator;

B8, repeat B1～B7, calculate total similarity Score of all detection windows in remote sensing images P, obtain total similarity Score distribution plan S;

C, in total similarity Score distribution plan S, have the total similarity Score that is greater than setting threshold, judge in surveyed area and have aircraft, its corresponding detection window position in remote sensing images is aircraft location.

2. the remote sensing based on geometry parts and radial gradient angle according to claim 1 detects the method for aircraft, it is characterized in that the two-value profile P to known aircraft in described steps A 2 _bthe specific practice of carrying out Its Sparse Decomposition is:

1) generate former word bank D how much

A, construct oval former word bank D _e:

Define oval atomic diagram as g _e(x, y) is as follows:

In formula, x, y is respectively that oval atomic diagram is as g _e(x, y) horizontal ordinate and ordinate in correcting image, oval atomic diagram is as g _ethe two-value profile P of the size of (x, y) and known aircraft _bit is identical,

for oval atomic diagram is as g _eelliptic region in (x, y), by following formula, calculated:

In formula, u, v be respectively horizontal ordinate x and ordinate y with oval atomic diagram as g _e(x, y) center is that the point of rotation turns clockwise

after new horizontal ordinate and ordinate, M _pBand N _pBbe respectively the two-value profile P of known aircraft _blength and width; (px, py) is elliptic region

center is to the two-value profile P of known aircraft _bthe relative coordinate at center, px ∈ [M _pB/ 2, M _pB/ 2], py ∈ [N _pB/ 2, N _pB/ 2]; A and b are respectively elliptic region major axis and minor axis,

for elliptic region

principal direction,

By parameter a, b,

px, py gets the oval atomic diagram that generates all over above all values as g _e(x, y), forms oval former word bank D _e:

B, construct trapezoidal former word bank D _t:

Define trapezoidal atomic diagram as g _t(x, y) is as follows:

In formula, x, y is respectively that trapezoidal atomic diagram is as g _t(x, y) horizontal ordinate and ordinate in correcting image, trapezoidal atomic diagram is as g _tthe two-value profile P of the size of (x, y) and known aircraft _bidentical; for trapezoidal atomic diagram is as g _ttrapezoid area in (x, y), is surrounded by four straight lines, by following formula, is calculated:

In formula, u, v is respectively that horizontal ordinate x and ordinate y justify atomic diagram as g with ladder _t(x, y) center is that the point of rotation turns clockwise

after horizontal ordinate and ordinate, ub, lb and h are respectively trapezoidal upper base, go to the bottom and high,

f is upper base center and the side-play amount at the center of going to the bottom, regulation upper base center toward left avertence for negative, toward right avertence for just, f ∈ [50,50];

with px, the definition of py and span and oval atomic diagram are as g _e(x, y) is identical;

By parameters u b, lb, h, f,

px, py gets the trapezoidal atomic diagram that generates all over all values as g _t(x, y), forms trapezoidal former word bank D _t:

C, by the former word bank D of all ellipses _ein atomic diagram as g _e(x, y) and trapezoidal former word bank D _tatom image g _t(x, y) merges, and generates the two-value profile P of known aircraft _bthe former word bank D of geometry:

D=D _e∪D _t

D, by all atomic diagram in how much former word bank D as g _e(x, y), g _t(x, y) renames numbering, is expressed as g _j(x, y), j=1,2 ..., N, N be atomic diagram in how much former word bank D as number, j renames atomic diagram after numbering as g _jthe index number of (x, y);

2) set up the optimal model of aircraft profile rarefaction representation:

min||Ω|| ₀

$s.t.{\left|\right|P_B-\underset{i\&Element;\mathrm{\&Omega;}}{\mathrm{\&Sigma;}}{g}_i\left|\right|}_2<\mathrm{\&sigma;}$

<g _i,g _j>=0,i≠j

In formula, || || ₀for 0-norm, the element number of expression set, || || ₂for 2-norm, represent that energy, < > are that indicate satisfied condition, σ of inner product operator, s.t. is energy threshold, get the two-value profile P of known aircraft _b15% of area, i.e. σ=15% * Area (P _b), Area () is that area operator, Ω are the subspace in the former word bank D of geometry satisfying condition,

3) adopt quantum genetic algorithm to solve above optimal model, obtain optimum atom image combining

be i optimum atomic diagram picture, N ^optfor optimum atomic diagram picture

number, i be optimum atomic diagram picture

index number.

3. the remote sensing images Airplane detection algorithm based on how much parts and radial gradient angle according to claim 1, is characterized in that, the employing in described step B2 radially-gradient angle Voting Algorithm calculates subimage block P _subprincipal direction ξ _subspecific practice be:

A, extraction subimage block P _subtwo-value profile P with known aircraft _bhistograms of oriented gradients feature H _sub(m, n, γ) and H _b(m, n, γ):

H _sub(m,n,γ)=HOG(P _sub)

H _B(m,n,γ)=HOG(P _B)

In formula, m, n, γ is respectively the horizontal ordinate in histograms of oriented gradients, ordinate and gradient direction;

B, be calculated as follows subimage block P _subtwo-value profile P with known aircraft _bmain gradient direction figure

with

$H_{\mathrm{sub}}^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_{\mathrm{sub}}(m,n,\mathrm{\&gamma;})$

$H_B^M(m,n)=\underset{\mathrm{\&gamma;}}{\max }{H}_B(m,n,\mathrm{\&gamma;})$

C, by subimage block P _submain gradient direction figure

two-value profile P with known aircraft _bmain gradient direction figure

be converted to polar form, obtain subimage block P _subdirection gradient polar plot PH _subthe two-value profile P of (r, θ) and known aircraft _bdirection gradient polar plot PH _b(r, θ), wherein, r, θ is respectively utmost point footpath coordinate and the polar angle coordinate of direction gradient polar plot;

D, press following formula antithetical phrase image block P _subdirection gradient polar plot PH _sub(r, θ) screens, and obtains candidate direction gradient-pole coordinate diagram C (r, θ):

$C(r,\mathrm{\&theta;})=\left\{\begin{array}{cc}{\mathrm{PH}}_{\mathrm{sub}}(r,\mathrm{\&theta;}),& \underset{\mathrm{\&phi;}\&Element;\mathrm{\&Phi;}}{\inf }\left(\right|{\mathrm{RGA}}_{\mathrm{sub}}(r,\mathrm{\&theta;})-{\mathrm{RGA}}_B(r,\mathrm{\&phi;})\left|\right)<\mathrm{\&epsiv;}\\ -10,& \mathrm{otherwise}\end{array}\right.$

In formula, inf () is that infimum operator, ε are threshold value, get 0.1-0.2, Φ=θ | PH _b(r, θ) ≠-10} is the two-value profile P of known aircraft _bdirection gradient polar plot PH _bin (r, θ), with subimage block P _subdirection gradient polar plot PH _sub(r, θ) has same pole footpath r, and gradient direction value is not-10 polar angle collection, RGA _sub(r, θ) and RGA _b(r, θ) is respectively subimage block P _subtwo-value profile P with known aircraft _bradially-gradient angle, be calculated as follows:

RGA _sub(r,θ)=|PH _sub(r,θ)-θ|

RGA _B(r,θ)=|PH _B(r,θ)-θ|

E, make gradient direction in candidate direction gradient-pole coordinate diagram C (r, θ) for-10 point is available point, and be calculated as follows out the prediction rotation angle R (k) of available point:

$R\left(k\right)={\mathrm{\&theta;}}_k^{\mathrm{sub}}-{\mathrm{\&theta;}}_k^B,k=\mathrm{1,2},...,N_C$

In formula, the index number that k is available point, N _cfor the number of available point,

be k the corresponding polar angle of available point,

two-value profile P for known aircraft _bdirection gradient polar plot PH _bpoint in (r, θ) in radially-gradient angle and candidate direction gradient-pole coordinate diagram

the corresponding polar angle of immediate point, wherein

for candidate direction gradient-pole coordinate diagram mid point

radially-gradient angle;

F, by following formula to the statistics of voting of the rotation angle in the prediction rotation angle R (k) of available point, obtain the histogram His of rotation angle _r:

His _R(λ)=#{k|R(k)=λ}

In formula, # () represents the number of set.

His _rthe direction of middle votes maximum is subimage block P _subprincipal direction ξ _sub, that is:

Images