CN102968799A - Integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method - Google Patents

Abstract

The invention provides an integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method, comprising the following steps of: (1) providing a G0 distribution-based self-adaptive global threshold CFAR pre-segmentation algorithm used for generating a target index matrix by combining the statistical property of data; (2) providing an integral image-based G0 distribution statistical parameter quick estimation method, wherein the statistical parameter can be calculated through simple operations such as addition and subtraction once 2-order and 4-prder integral images of an original image are obtained during the implementation of the method; and (3) giving out a basic implementation process of the ACCA-CFAR SAR image target detection method. Through the integral image-based G0 distribution statistical parameter quick estimation strategy provided by the invention, the time efficiency of the method can be greatly improved and the time complexity of the method is irrelevant to the size of a sliding window; and the requirement of the existing automatic target recognition (ATR) system on the treatment of large-scene data can be met to a great extent.

Description

A kind of quick A CCA-CFAR SAR image object detection method based on integral image

Technical field

The present invention relates to SAR image interpretation technical field, be specifically related to a kind of quick A CCA-CFARSAR image object detection method based on integral image.

Background technology

High resolving power, large scene synthetic-aperture radar (Synthetic Aperture Radar, SAR) image emerges in large numbers, provide possibility for being applied even more extensively the SAR image, brought new challenge for simultaneously SAR image interpretation technology, in being adapted to before some, low resolution, the treatment technology of little scene SAR image is no longer applicable.As one of SAR image interpretation technology of key, target detection has a significant impact performance and the efficient tool of the subsequent treatment such as feature extraction, target identification and classification.At present in existing certain development aspect this, CFAR (Constant False Alarm Rate, CFAR) detecting is the object detection method that wherein is most widely used, its ultimate principle is: the energy according near the reference unit estimated background clutter the detecting unit is also adjusted thresholding successively, so that false-alarm probability constant (referring to document [1] Xu Xiaojian, Huang Peikang, " radar system and information processing thereof; " the Electronic Industry Press, 2010.).

But, traditional CFAR operator, be cell-average (Cell Averaging, CA)-CFAR(is referring to document [2] L.M.Novak, G.J.Owirka, W.S.Brower, and A.L.Weaver, " The automatic target-recognition system inSAIP " Linc.Lab.J., Vol.10, No.2,1997, pp.187-202.), suppose the background clutter Gaussian distributed, the single goal that is only applicable in the local uniform background clutter detects, and is non-homogeneous or when comprising multiple goal, it detects performance and sharply descends when background clutter.In order to satisfy the demand of practical engineering application, in the urgent need to high resolving power, data content complexity and the huge characteristics of capacity of obtaining data for novel sensor, the SAR imaging characteristic difference of combining target and background clutter, research is applicable to the quick CFAR object detection method of various complex scenes.

The technical scheme of prior art one:

In order to satisfy the application demand for the treatment of S AR image complex scene, there is the researcher to consider the GO (Greatest Of) of each tool relative merits-CFAR(referring to document [3] V.G., Hansen, " Constant false alarm rate processing insearch radars; " In Proceedings of the IEEE1973International Radar Conference, London, 1973, pp.325-332.), SO (Smallest Of)-CFAR(is referring to document [4] G.V.Trunk, " Range resolution of targets usingautomatic detectors; " IEEE Transactions on Aerospace and Electronic Systems, AES-14, Sept.1978, pp.750-755.) etc. basic operator combine according to certain criterion.Wherein, the most representative VI (VariabilityIndex)-CFAR(is referring to document [5] M.E.Smith and P.K.Varshney, " Intelligent CFAR processor based on data variability; " IEEE Trans.Aerosp.Electron.Syst., Vol.36, No.3, Jul.2000, pp.837 – 847.) operator, namely according to index value and test of hypothesis average ratio based on the second-order statistics feature, judge and select CA-CFAR that one of GO-CFAR and SO-CFAR operator carry out target detection, so have three's advantage concurrently, be applicable to process even scene, and comprise multiple goal, the complex scene data of clutter edge etc.

Adopt similar thinking, document [6] (G.Gao, L.Liu, L.J.Zhao, G.T.Shi, et al., " An adaptive and fastCFAR algorithm based on automatic censoring for target detection in high-resolution SARimages; " IEEE Trans.Geosci.Remote Sens., Vol.47, No.6,2009, pp.1685-1697.) a kind of quick CFAR object detection method based on automatic retrieval (Automatic Censoring, AC) is proposed.As Fig. 1 center 1. shown in, the pre-segmentation algorithm of the method by a kind of based target degree of confidence generates the target index matrix and comes candidate target pixel in the mark backdrop window, so that they are rejected from reference unit, improve operator for the applicability of multiple goal scene, the light grey square frame among the figure namely represents target index pixel.How to calculate quickly and efficiently the key that the object pixel index value is these class methods.

The shortcoming of prior art one:

Although by a plurality of basic operator dominance complementations can be improved the CFAR algorithm for the applicability of complex scene, still there is following shortcoming in these class methods:

(1) the VI-CFAR operator in the document [5] is not deeply considered the statistical property of time performance and the background clutter of algorithm.This operator adopts the index value computing method of processing based on local window, and its hypothesis background clutter Gaussian distributed is determined index value by average and the variance of estimating reference unit.Calculation of complex, time efficiency are lower, and selected empirical model is not suitable for non-homogeneous data.

(2) the global threshold pre-segmentation algorithm of the based target degree of confidence of document [6] employing is not considered the statistical property of data, its performance only depends on objective degrees of confidence, be the object pixel number and the ratio of the total number of pixels of image, thereby Algorithm Performance is relatively more responsive to the value of this parameter.

The technical scheme of prior art two:

Part researcher will more can accurately be reflected the statistical model introducing CFAR operator of background clutter statistical property, to improve the target detection ability of algorithm under complex scene.Wherein, it is that a kind of compound Gauss model of the SAR of meeting scattering mechanism is (referring to document [7] E.Jakeman and P.N.Pusey that K-distributes, " A model for non-Rayleigh sea echo; " IEEE Trans.Antennas Propagat., Vol.AP-24,1976, pp.806-814. and document [8] Hao Chengpeng, Hou Chaohuan, the DP-CFAR detecting device [J] under a kind of K-Distribution Clutter background, electronics and information journal, Vol.29, No.3,2007, pp.756-759.), it is because having extra large clutter, the forest land, the ability of the non-homogeneous data modeling such as farmland and more concerned; On the other hand, G ⁰Distribute (referring to document [9] A.C.Frery, H.J.Muller, C.C.F.Yanasse, and S.J.S.Sant ' Anna, " A model forextremely heterogeneous clutter; " IEEE Trans.Geosci.Remote Sens., Vol.35, No.3, May1997, pp.648-659.) have the ability that extensive uniformity coefficient is changed the regional modeling of lower clutter, stronger model compatibility and calculation of parameter are simple.As Fig. 1 center 2. shown in, document [6] is applied to the CFAR operator with this statistical model, fully utilizes this model and is applicable to equal, the non-homogeneous and characteristic of non-homogeneous scene data modeling is extremely guaranteed the ability of algorithm process complex scene data.

The shortcoming of prior art two:

Although it is widely used statistical model aspect the research of CFAR operator that K-distributes, along with the raising of SAR image resolution ratio and the increase of content complexity, still there is following shortcoming in this class algorithm:

(1) the K-distribution is not suitable for the extremely Nonuniform Domain Simulation of Reservoir modelings such as open marine site to the city in the High Resolution SAR Images, inshore marine site or high sea condition.Fig. 2 and Fig. 3 take the interested marine site that intercepts from 3m-resolution, large scene SAR image and inshore city (ROI) as test data, have contrasted K-and have distributed and G respectively ⁰The distribution range model (namely Distribution) modeling result.Can find out,

Distribution all is better than the K-distribution to a certain extent for the modeling result of these two groups of data, and especially for non-homogeneous data of extreme such as inshore cities, the performance that K-distributes sharply descends, and Distribute and still can obtain preferably fitting result.

(2) compare and G ⁰Distribute the statistical parameter that K-distributes and the equal more complicated of calculating of local CFAR threshold value.

The technical scheme of prior art three:

For the demand that satisfies real-time automatic target identification (Automatic Target Recognition, ATR) system and process the large scene data, the time efficiency that improves the CFAR operator is another problem demanding prompt solution.But, still be in the junior stage in the research aspect this at present, the simplest accelerated method is larger moving window scanning step to be set (referring to document [10] C.H.Jung, W.Y.Song, S.H.Rho, et al., " Double-step fast CFAR scheme for multiple target detectionin high resolution SAR images; " IEEE, 2010, pp.1172-1175.); Document [6] proposes a kind of G ⁰The distribution statistics parameter is estimated strategy fast.Consider two-parameter moving window as shown in Figure 4, wherein h is the size of moving window, and r represents the width of backdrop window, and protecting window is of a size of h-2r.It is identical to have most of reference units in conjunction with (or up and down) about the CFAR operator two adjacent window apertures; only backdrop window and the different characteristics of the individual reference unit of protecting window boundary h+ (h-2r); take from left to right scan method as example; can be according to strategy as shown in Figure 5; avoid some repetitive operations, improve the time performance of algorithm.

${{\mathrm{\μ}}^2}_{\mathrm{right}}=\frac{N_C\·{{\mathrm{\μ}}^2}_{\mathrm{left}}-(\underset{i=1}{\overset{h}{\mathrm{\Σ}}}{x}_{l,i}^2+\underset{i=1}{\overset{h-2r}{\mathrm{\Σ}}}{x}_{r,i}^2)+\left(\underset{i=1}{\overset{h}{\mathrm{\Σ}}}{x}_{r,i}^2+\underset{i=1}{\overset{h-2r}{\mathrm{\Σ}}}{x}_{l,i}^2\right)}{N_C}---\left(A\right)$

${\mathrm{\μ}}_{\mathrm{right}}^4=\frac{N_C\·{\mathrm{\μ}}_{\mathrm{left}}^2-(\underset{i=1}{\overset{h}{\mathrm{\Σ}}}{x}_{l,i}^4+\underset{i=1}{\overset{h-2r}{\mathrm{\Σ}}}{x}_{r,i}^4)+(\underset{i=1}{\overset{h}{\mathrm{\Σ}}}{x}_{r,i}^4+\underset{i=1}{\overset{h-2r}{\mathrm{\Σ}}}{x}_{l,i}^4)}{N_C}---\left(B\right)$

In the formula,

With With

The 2-rank that represent respectively effective reference unit in the left and right backdrop window, 4-rank sample moment; x _{L, i}And x _{R, i}Then correspond respectively to the reference unit at left and right window edge place; " subtract " item corresponding to the Dark grey dash area among Fig. 5, " adding ", Xiang Ze was corresponding to light grey dash area.

Suppose all N in the whole backdrop window _CIndividual pixel is background pixel, and then complexity computing time of fast method is (N in the document [6] ²-1) (3h+12)+3N _C+ 6, the time complexity of traditional C FAR operator is N ²(3N _C+ 2).Therefore the fast method in the document [6] can be down to time complexity the 1/4r of traditional C FAR operator.

The shortcoming of prior art three:

Although the express statistic parameter estimation strategy in the document [6] is by avoiding some repetitive operations, improved the time efficiency of algorithm on largely.But described in document [6], when the moving window parameter is set to h=71, r=20, picture size is 1375 * 1880 o'clock, still be 40.0471s the working time of this algorithm, and this algorithm still can not satisfy the application demand of the processing of large scene data and real-time ATR system far away as can be known.

Summary of the invention

Technical matters to be solved by this invention is: the deficiency for existing CFAR object detection method, a kind of new quick A CCA-CFAR SAR image object detection method is proposed, and comprising: (1) proposes a kind of based on G in conjunction with the statistical property of data ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes is used for generating the target index matrix.(2) a kind of G based on integral image is proposed ⁰Distribution statistics parameter method for quick estimating in the method implementation procedure, in case try to achieve 2-rank, the 4-rank integral image of original image, can be tried to achieve statistical parameter by the computing such as simply adding, subtract, thereby greatly improve the time efficiency of algorithm; (3) provided the basic realization flow of this ACCA-CFAR algorithm of target detection.In addition, the algorithm owing to pre-segmentation part and succeeding target test section is based on G ⁰The CFAR operator that distributes, just the former is based on global threshold, and the latter is local threshold, thereby both implementation methods are similar, thus the integral body that has reduced algorithm realizes difficulty.

The technical solution adopted in the present invention is as follows: a kind of quick A CCA-CFAR SAR image object detection method based on integral image, and the method is specific as follows:

(1) in conjunction with the statistical property of data, utilizes based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes generates the target index matrix;

(2) employing is based on the G of integral image ⁰Distribution statistics parameter method for quick estimating wherein, is tried to achieve after the 2-rank, 4-rank integral image of original image, can try to achieve statistical parameter by the computing such as simply adding, subtract;

(3) by the quick A CCA-CFAR algorithm based on integral image, realize that the SAR image object detects.

Wherein, described utilization is based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes generates the target index matrix, and its concrete steps are as follows:

Step 11: invariable false alerting p is set _Fa, two-parameter moving window is extended to entire image, and protecting window is set is of a size of 0;

Step 12: the reference unit in the backdrop window is used for G ⁰Distribution range modeling statistics parameter estimation,

In the formula, the n presentation video look number; μ ², μ ⁴Represent respectively 2-rank, the 4-rank sample moment of reference unit, their account form is:

${\mathrm{\μ}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\Σ}}}{\left|z\left(t\right)\right|}^m---\left(3\right)$

In the formula, m represents exponent number, and z (t) is each reference unit, N _sExpression reference unit number;

Step 13: according to CFAR operator ultimate principle formula, calculate global threshold T _g,

$1-p_{\mathrm{fa}}={\∫}_0^{T_g}{f}_{G^0}\left(z\right)\mathrm{dz}---\left(4\right)$

In the formula,

Expression G ⁰The probability density function of distribution range model;

$f_{G^0}\left(z\right)=\frac{2n^n\mathrm{\Γ}(n-\mathrm{\α}){\mathrm{\γ}}^{-\mathrm{\α}}{z}^{2n-1}}{\mathrm{\Γ}\left(n\right)\mathrm{\Γ}(-\mathrm{\α}){(\mathrm{\γ}+n{z}^2)}^{n-\mathrm{\α}}}---\left(5\right)$

In the formula, alpha, gamma is respectively shape and scale parameter ,-α＞0, γ＞0; Work as n=1, namely during the haplopia data, the analytic solution of formula (4) are:

For looking data, because formula (4) without analytic solution, adopts dichotomy to determine more

Step 14: according to the criterion shown in the formula (7), in the original image to each test pixel I _tAdjudicate,

H wherein ₁And H ₀Represent respectively object pixel hypothesis and background pixel hypothesis, thus the target index matrix M shown in the production (8):

$m(x,y)=\left\{\begin{array}{c}1,H_1\\ 0,H_0\end{array}\right.---\left(8\right)$

The coordinate position of (x, y) expression pixel in the formula.

Wherein, described employing is based on the G of integral image ⁰Distribution statistics parameter method for quick estimating, its concrete steps are as follows:

Step 21: by repeatedly carrying out following two groups of computings, the m-rank image i of an original image i of scanning (x, y) ^m(x, y) generates its m-rank integral image ii ^m(x, y),

s(x,y)=s(x,y-1)+i ^m(x,y) (9)

ii ^m(x,y)=ii ^m(x-1,y)+s(x,y) (10)

In the formula, the coordinate position of (x, y) expression current pixel point;

I wherein _k()=i () (k=1 ..., m), " dot product " of Π presentation video; S (x, y) represents i ^m(x, y) follows integration, s (x ,-1)=0 wherein, ii ^m(1, y)=0; According to the needs that statistical parameter is estimated, get m=2,4;

Step 22: based on the m-rank integral image of original image, calculate fast the m-rank sample moment of reference unit in any two-parameter moving window,

${\mathrm{\μ}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\Σ}}}{\left|z\left(t\right)\right|}^m$ (11)

$=\frac{1}{N_s}\left\{\right[({\mathrm{ii}}_1^m+{\mathrm{ii}}_4^m)-({\mathrm{ii}}_2^m+{\mathrm{ii}}_3^m)]-[({\mathrm{ii}}_{1^{\′}}^m+{\mathrm{ii}}_{4^{\′}}^m)-({\mathrm{ii}}_{2^{\′}}^m+{\mathrm{ii}}_{3^{\′}}^m)\left]\right\}$

In the formula,

(p=1,2,3,4,1', 2', 3', 4') is the pixel value of summit p place m-rank integral image;

Corresponding to pixel value sum in the whole moving window, Be pixel value sum in the protecting window;

Step 23: with 2-rank, 4-rank sample moment substitution formula (1) and (2), can try to achieve G by simple operation ⁰The statistical parameter of model.

Wherein, described by the quick A CCA-CFAR algorithm based on integral image, realize that the SAR image object detects, its specific implementation step is as follows:

Step 301: adopt based on G ⁰The global threshold CFAR algorithm that distributes carries out the target pre-segmentation, generates the target index matrix; For the ease of follow-up computing, to this index matrix negate, obtain the background pixel index matrix, wherein background dot is 1, impact point is 0, is used for marking all candidate background pixels;

Step 302: by with background pixel index matrix and original image dot product, remove the interference of candidate target pixel, obtain the background clutter image, namely with the reservation background pixel value of original image equidimension, the image of target pixel value zero setting;

Step 303: the m-rank integral image that calculates the background clutter image;

Step 304: invariable false alerting and moving window parameter are set, and wherein the size of protecting window should be greater than the size of target to be detected, and sliding window size should participate in the statistical parameter estimation to ensure abundant background pixel fully greatly;

Step 305: utilize moving window to pursue the picture element scan original image, and by m-rank integral image and background pixel index matrix, calculate the m-rank sample moment of background pixel;

Step 306: utilize the m-rank sample moment of background pixel, adopt the MoM method to estimate G ⁰The statistical parameter that distributes;

Step 307: utilize statistical parameter to calculate the CA-CFAR local threshold;

Step 308: by the size of compare test unit and local threshold, judge that whether this unit is the candidate target pixel, is to be 1, otherwise is 0;

Step 309: judge whether to continue scanning, if do not scan complete image, then skip to step 305, scan next test cell; Otherwise, carry out next step;

Step 310: remove false-alarm by subsequent treatment, merge the object pixel zone, thereby obtain final target detection result.

Wherein, the subsequent treatment described in the step 310 is processed or the target area cluster for counting wave filter, morphology.

The beneficial effect that technical solution of the present invention is brought is:

Compare and existing CFAR object detection method, the beneficial effect that the quick A CCA-CFAR method based on integral image proposed by the invention is brought embodies in the following areas:

(1) by introducing AC technology and G ⁰Distributed model has the target detection ability in the SAR image complex scene;

(2) propose based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes has been considered time efficiency and the data statistics characteristic of algorithm, to the sensitivity of parameter lower and its realize that principle is similar to the realization principle of succeeding target test section, thereby reduced the integral body realization difficulty of algorithm;

What (3) propose can improve the time efficiency of algorithm greatly based on the strategies of integral image, and this fast method is so that the time complexity of algorithm and moving window cache oblivious, thereby can in the situation of influence time efficient not, set according to actual needs enough large sliding window size with the accuracy of assurance clutter statistical characteristics modeling.

Description of drawings

Fig. 1 is based on the self-adaptation of AC, the basic ideas of quick CFAR algorithm of target detection in the document [6];

Fig. 2 is

Distribution, K-distribute to the modeling result contrast of extra large clutter in the High Resolution SAR Images; (a) extra large clutter ROI; (b) extra large clutter PDF with

Distribution, K-distribution PDF contrast;

Fig. 3 is Distribution, K-distribute to the modeling result contrast of land clutter in the High Resolution SAR Images; (a) land clutter ROI; (b) land clutter PDF with

Distribution, K-distribution PDF contrast;

Fig. 4 is two-parameter moving window synoptic diagram;

Fig. 5 is G in the document [6] ⁰The distribution statistics parameter is estimated synoptic diagram fast;

Fig. 6 is based on G ⁰The process flow diagram of the self-adaptation global threshold CFAR pre-segmentation algorithm that distributes;

Fig. 7 is the G based on integral image ⁰The process flow diagram of distribution statistics parameter method for quick estimating;

Fig. 8 is m-rank integral image synoptic diagram;

Fig. 9 is based on reference unit m-rank sample moments fast calculation synoptic diagram in the two-parameter moving window of integral image;

Figure 10 is the process flow diagram based on the quick A CCA-CFAR algorithm of target detection of integral image;

Figure 11 is the original image synoptic diagram;

Figure 12 is the target detection result of institute of the present invention extracting method, reaches contrast (a) the target thumbnail with the middle method of document [6]; (b) based on the quick A CCA-CFAR target detection result of integral image; (c) based on the final target detection result of the quick A CCA-CFAR method of integral image; (d) the final target detection result of method in the document [6].

Embodiment

Further specify the present invention below in conjunction with accompanying drawing and instantiation.

The present invention proposes a kind of quick A CCA-CFAR SAR image object detection method based on integration image, specifically comprises the steps:

(1) based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes

Consider on the one hand G ⁰The above-mentioned advantage that distributes; On the other hand since in the SAR image ratio of object pixel generally very little, estimate if utilize mass data to carry out statistical parameter, object pixel to affect meeting very little, and ACCA-CFAR operator itself is not very high to the accuracy requirement of pre-segmentation algorithm.Therefore, proposition is a kind of based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes is used for generating the target index matrix.This pre-segmentation algorithm flow chart as shown in Figure 6, concrete methods of realizing is as follows:

Step 1: shown in Fig. 6 center 1, invariable false alerting p is set _Fa, two-parameter moving window shown in Figure 4 is extended to entire image, and protecting window is set is of a size of 0.

Step 2: shown in Fig. 6 center 2, the reference unit in the backdrop window is used for G ⁰Distribution range modeling statistics parameter estimation,

In the formula, the n presentation video look number; μ ², μ ⁴Represent respectively 2-rank, the 4-rank sample moment of reference unit, their account form is:

${\mathrm{\μ}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\Σ}}}{\left|z\left(t\right)\right|}^m---\left(3\right)$

In the formula, m represents exponent number, and z (t) is each reference unit, N _sExpression reference unit number.Formula (1) and (2) be adopt method of moment (MoM) (referring to document [11] He Zhiguo, Zhou Xiaoguang, ground force and Kuang Guangyao, " a kind of based on G ⁰The quick CFAR detection method of SAR image that distributes, " National University of Defense technology's journal, Vol.31, No.1,2009, the G that pp.47-51.) obtains ⁰Distribution range modeling statistics parameter calculation formula.

Step 3: shown in Fig. 6 center 3, according to CFAR operator ultimate principle formula, calculate global threshold T _g,

$1-p_{\mathrm{fa}}={\∫}_0^{T_g}{f}_{G^0}\left(z\right)\mathrm{dz}---\left(4\right)$

In the formula,

Expression G ⁰The probability density function of distribution range model (PDF) (referring to document [9]).

$f_{G^0}\left(z\right)=\frac{2n^n\mathrm{\Γ}(n-\mathrm{\α}){\mathrm{\γ}}^{-\mathrm{\α}}{z}^{2n-1}}{\mathrm{\Γ}\left(n\right)\mathrm{\Γ}(-\mathrm{\α}){(\mathrm{\γ}+n{z}^2)}^{n-\mathrm{\α}}}---\left(5\right)$

In the formula, alpha, gamma is respectively shape and scale parameter ,-α＞0, γ＞0.Work as n=1, namely during the haplopia data, the analytic solution of formula (4) are:

For looking data, because formula (4) without analytic solution, adopts dichotomy to determine more

(referring to document [11]).

Step 4: shown in Fig. 6 center 4, according to the criterion shown in the formula (7), in the original image to each test pixel I _tAdjudicate,

H wherein ₁And H ₀Represent respectively object pixel hypothesis and background pixel hypothesis, thus the target index matrix M shown in the production (8).

$m(x,y)=\left\{\begin{array}{c}1,{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA00002575762400021.png,HDA00002575762400031.png"\; state="\{\{state\}\}">H</figure-callout>}}_1\\ 0,H_0\end{array}\right.---\left(8\right)$

The coordinate position of (x, y) expression pixel in the formula.

(2) based on the G of integral image ⁰Distribution statistics parameter method for quick estimating

By formula (1)-(3) as can be known, G ⁰The main computing of distribution statistics parameter estimation is the calculating of reference unit 2-rank, 4-rank sample moment, and the main operand of sample moment is " summation ", if therefore can partly accelerate summation operation, improves surely the integral operation speed of algorithm of target detection.Utilize integral image techniques can calculate fast rectangular characteristic (referring to document [12] P.Viola, M.Jones, " Robust real-time face detection; " International Journal of Computer Vision, Vol.52, No.2,2004, pp.137-154.), can be introduced into the sample moments fast calculation.Fig. 7 is the G based on integral image ⁰The distribution statistics parameter is estimated process flow diagram fast, and its concrete methods of realizing is as follows:

Step 1: shown in Fig. 7 center 1, by repeatedly carrying out following two groups of computings, the m-rank image i of an original image i of scanning (x, y) ^m(x, y) generates its m-rank integral image ii ^m(x, y), Fig. 8 are the synoptic diagram of m-rank integral image.

s(x,y)=s(x,y-1)+i ^m(x,y) (9)

ii ^m(x,y)=ii ^m(x-1,y)+s(x,y) (10)

In the formula, the coordinate position of (x, y) expression current pixel point;

I wherein _k()=i () (k=1 ..., m), " dot product " of Π presentation video; S (x, y) represents i ^m(x, y) follows integration, s (x ,-1)=0 wherein, ii ^m(1, y)=0; According to the needs that statistical parameter is estimated, get m=2,4.

Step 2: shown in Fig. 7 center 2, in conjunction with the m-rank integral image of Fig. 9 based on original image, calculate fast the m-rank sample moment of reference unit in any two-parameter moving window (light grey dash area),

${\mathrm{\&mu;}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{\left|z\left(t\right)\right|}^m$ (11)

$=\frac{1}{N_s}\left\{\right[({\mathrm{ii}}_1^m+{\mathrm{ii}}_4^m)-({\mathrm{ii}}_2^m+{\mathrm{ii}}_3^m)]-[({\mathrm{<figure-callout\; id="1"\; label="ii"\; filenames="HDA00002575762400021.png,HDA00002575762400031.png"\; state="\{\{state\}\}">ii</figure-callout>}}_{1^{\&prime;}}^m+{\mathrm{<figure-callout\; id="4"\; label="ii"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">ii</figure-callout>}}_{4^{\&prime;}}^m)-({\mathrm{<figure-callout\; id="2"\; label="ii"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">ii</figure-callout>}}_{2^{\&prime;}}^m+{\mathrm{<figure-callout\; id="3"\; label="ii"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">ii</figure-callout>}}_{3^{\&prime;}}^m)\left]\right\}$

In the formula,

(p=1,2,3,4,1', 2', 3', 4') is the pixel value of summit p place m-rank integral image among Fig. 9;

Corresponding to pixel value sum in the whole moving window,

Be pixel value sum in the protecting window.

Step 3: shown in Fig. 7 center 3, with 2-rank, 4-rank sample moment substitution formula (1) and (2), can try to achieve G by simple operation ⁰The statistical parameter of model.

As shown in table 1, complexity computing time of this statistical parameter method for quick estimating is 2 (N-1) ²+ 24N ²Complexity computing time of strategies is respectively with the ratio of this value in traditional C FAR operator and the document [6]:

${\mathrm{\&upsi;}}_1>\frac{N^2({3N}_C+2)}{2{(N-1)}^2+{24\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">N</figure-callout>}}^2}>\frac{N^2({3N}_C+2)}{{26\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">N</figure-callout>}}^2}=\frac{{3N}_C+2}{26}\&ap;\frac{r(h-r)}{2}---\left(12\right)$

${\mathrm{\&upsi;}}_2>\frac{(N^2-1)(3h+12)+{3N}_C+6}{2{(N-1)}^2+{24\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">N</figure-callout>}}^2}>\frac{N^2(3h+12)}{{26\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="HDA00002575762400021.png,HDA00002575762400052.png"\; state="\{\{state\}\}">N</figure-callout>}}^2}=\frac{3h+2}{26}\&ap;\frac{h}{9}---\left(13\right)$

Analysis of complexity computing time of the fast method that table 1 is proposed by the invention

As can be known, the time efficiency of institute of the present invention extracting method is r (h-r)/2 times of traditional double parameters C FAR algorithm at least, the h/9 of document [6] algorithm of carrying doubly, and the time complexity of institute of the present invention extracting method and the size of window are irrelevant, and the time complexity of other two kinds of methods is then closely related with window parameter.For the stability that guarantees that statistical parameter is estimated, moving window and backdrop window size must arrange enough large usually.Relatively be set to example with the parameter in the document [6] for convenient, i.e. h=71, r=20, the time efficiency that can calculate institute of the present invention extracting method this moment is at least about 510 times of traditional double parameters C FAR algorithm, 8 times of document [6] algorithm of carrying.

(3) based on the basic procedure of the quick A CCA-CFAR SAR image object detection method of integral image

Figure 10 is the process flow diagram of quick A CCA-CFAR algorithm proposed by the invention, and its specific implementation step is as follows:

Step 1: shown in Figure 10 center 1a and 1b, adopt based on G ⁰The global threshold CFAR algorithm that distributes carries out the target pre-segmentation, generates the target index matrix; For the ease of follow-up computing, to this index matrix negate, obtain the background pixel index matrix, wherein background dot is 1, impact point is 0, is used for marking all candidate background pixels.

Step 2: shown in Figure 10 center 2a and 2b, by with background pixel index matrix and original image " dot product ", remove the interference of candidate target pixel, obtain the background clutter image, namely with the reservation background pixel value of original image equidimension, the image of target pixel value zero setting.

Step 3: shown in Figure 10 center 3, calculate the m-rank integral image of background clutter image.

Step 4: shown in Figure 10 center 4, invariable false alerting and moving window parameter are set, wherein the size of protecting window should be greater than the size of target to be detected, and sliding window size should participate in the statistical parameter estimation to ensure abundant background pixel fully greatly.

Step 5: shown in Figure 10 center 5a and 5b, utilize moving window to pursue the picture element scan original image, and by m-rank integral image and background pixel index matrix, calculate the m-rank sample moment of background pixel.

Step 6: shown in Figure 10 center 6, utilize the m-rank sample moment of background pixel, adopt the MoM method to estimate G ⁰The statistical parameter that distributes.

Step 7: shown in Figure 10 center 7, utilize statistical parameter to calculate the CA-CFAR local threshold.

Step 8: shown in Figure 10 center 8, by the size of compare test unit and local threshold, judge that whether this unit is the candidate target pixel, is to be 1, otherwise is 0.

Step 9: shown in Figure 10 center 9, judge whether to continue scanning, if do not scan complete image, then skip to step 5, scan next test cell; Otherwise, carry out next step.

Step 10: shown in Figure 10 center 10, remove false-alarm by subsequent treatment (as: counting wave filter, morphology processing, target area cluster etc.), merge the object pixel zone, thereby obtain final target detection result.

Below by a concrete application process based on the quick A CCA-CFAR object detection method of integral image that provides for example that this invention proposes, compare with the fast method that document [6] proposes simultaneously.

Suppose to have obtained a width of cloth open Sea SAR image as shown in figure 11 by certain sensor, now need judge fast the position that whether comprises Ship Target in this image and determine them.These data are the single-view picture, and it is of a size of 961*680, and pixel resolution is 3m.

Suppose to arrange based on G according to technical requirement ⁰The invariable false alerting p of distribution self-adaptation global threshold CFAR pre-segmentation algorithm _Fa1=10 ^-2For the ease of contrast, based on the parameter of the quick A CCA-CFAR target detection of integral image adopt with document [6] in identical setting, i.e. invariable false alerting p _Fa2=10 ^-3, sliding window size h=71, r=20; The subsequent treatment parameter wavenumber filter threshold value of falling into a trap is 5, and the morphology processing parameter is 1, and method adopts identical therewith setting in the document as a comparison [6].

Figure 12 has provided the interim result of institute of the present invention extracting method, and has contrasted the final target detection result of method in itself and the document [6].Wherein, Figure 12 (a) is for adopting based on G ⁰The target thumbnail that the self-adaptation global threshold CFAR pre-segmentation algorithm that distributes generates, it has detected most of real goal candidate region in the very little situation of false alarm rate; Figure 12 (b) is the quick A CCA-CFAR target detection result based on integral image, in order to remove discrete false-alarm pixel wherein, processes the final target detection result who has obtained shown in Figure 12 (c) by counting filtering, morphology; Figure 12 (d) is the final target detection result of method in the document [6].Contrast Figure 12 (c) and Figure 12 (d), easily see: both testing results are suitable, all effectively detected three naval vessels.

Aspect the algorithm time efficiency, for the ease of relatively, in table 2, contrasted the working time of two kinds of methods.Yi Zhi: be about 10 times of fast method in the document [6] working time of institute of the present invention extracting method, this and aforementioned analysis are basically identical.On the other hand, the working time of two kinds of method pre-segmentations part is based on quite, but the former is because having considered the statistical property of data, accuracy of detection is higher, to the sensitivity of parameter lower and its realize that principle is basically identical with follow-up CFAR algorithm, thereby reduced the integral body realization difficulty of algorithm.

The time efficiency of method contrast in table 2 this invention institute's extracting method and the document [6]

Also can finish goal of the invention by following replacement scheme:

(1) the present invention is applicable to the target detection problems in the multiple SAR image scene such as marine site, land clutter;

(2) generating the pre-segmentation algorithm of target index matrix replaceable is global threshold pre-segmentation algorithm based on statistical models such as Gaussian distribution, K-distributions, and other adaptive thresholding algorithm;

(3) strategies based on integral image can be applied to other statistical model in the CFAR operator, and the MoM method that for example K-distributes, Pareto distributes and fractional order method of moment (MoFM) statistical parameter are estimated.

Claims (5)

1. quick A CCA-CFAR SAR image object detection method based on integral image is characterized in that the method is specific as follows:

(1) in conjunction with the statistical property of data, utilizes based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes generates the target index matrix;

(2) employing is based on the G of integral image ⁰Distribution statistics parameter method for quick estimating wherein, is tried to achieve after the 2-rank, 4-rank integral image of original image, can try to achieve statistical parameter by the computing such as simply adding, subtract;

(3) by the quick A CCA-CFAR algorithm based on integral image, realize that the SAR image object detects.

2. a kind of quick A CCA-CFAR SAR image object detection method based on integral image according to claim 1, it is characterized in that: described utilization is based on G ⁰The self-adaptation global threshold CFAR pre-segmentation algorithm that distributes generates the target index matrix, and concrete steps are as follows:

Step 11: invariable false alerting p is set _Fa, two-parameter moving window is extended to entire image, and protecting window is set is of a size of 0;

Step 12: the reference unit in the backdrop window is used for G ⁰Distribution range modeling statistics parameter estimation,

In the formula, the n presentation video look number; μ ², μ ⁴Represent respectively 2-rank, the 4-rank sample moment of reference unit, their account form is:

${\mathrm{\&mu;}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{\left|z\left(t\right)\right|}^m---\left(3\right)$

In the formula, m represents exponent number, and z (t) is each reference unit, N _sExpression reference unit number;

Step 13: according to CFAR operator ultimate principle formula, calculate global threshold T _g,

$1-p_{\mathrm{fa}}={\&Integral;}_0^{T_g}{f}_{G^0}\left(z\right)\mathrm{dz}---\left(4\right)$

In the formula,

Expression G ⁰The probability density function of distribution range model:

$f_{G^0}\left(z\right)=\frac{{2n}^n\mathrm{\&Gamma;}(n-\mathrm{\&alpha;}){\mathrm{\&gamma;}}^{-\mathrm{\&alpha;}}{z}^{2n-1}}{\mathrm{\&Gamma;}\left(n\right)\mathrm{\&Gamma;}(-\mathrm{\&alpha;}){(\mathrm{\&gamma;}+{\mathrm{nz}}^2)}^{n-\mathrm{\&alpha;}}}---\left(5\right)$

In the formula, alpha, gamma is respectively shape and scale parameter ,-α＞0, γ＞0; Work as n=1, namely during the haplopia data, the analytic solution of formula (4) are:

For looking data, because formula (4) without analytic solution, adopts dichotomy to determine more

Step 14: according to the criterion shown in the formula (7), in the original image to each test pixel I _tAdjudicate,

H wherein ₁And H ₀Represent respectively object pixel hypothesis and background pixel hypothesis, thus the target index matrix M shown in the production (8):

$M(x,y)=\left\{\begin{array}{cc}1& ,H_1\\ 0& ,H_0\end{array}\right.---\left(8\right)$

The coordinate position of (x, y) expression pixel in the formula.

3. a kind of quick A CCA-CFAR SAR image object detection method based on integral image according to claim 2, it is characterized in that: described employing is based on the G of integral image ⁰Distribution statistics parameter method for quick estimating, its concrete steps are as follows:

Step 21: by repeatedly carrying out following two groups of computings, the m-rank image i of an original image i of scanning (x, y) ^m(x, y) generates its m-rank integral image ii ^m(x, y),

s(x,y)＝s(x,y-1)+i ^m(x,y) (9)

ii ^m(x,y)＝ii ^m(x-1,y)+s(x,y) (10)

In the formula, the coordinate position of (x, y) expression current pixel point;

I wherein _k()=i (), k=1 ..., m, " dot product " of ∏ presentation video; S (x, y) represents i ^m(x, y) follows integration, s (x ,-1)=0 wherein, ii ^m(1, y)=0; According to the needs that statistical parameter is estimated, get m=2,4;

Step 22: based on the m-rank integral image of original image, calculate fast the m-rank sample moment of reference unit in any two-parameter moving window,

${\mathrm{\&mu;}}^m=\frac{1}{N_s}\underset{t=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{\left|z\left(t\right)\right|}^m$ (11)

$=\frac{1}{N_s}\left\{\right[({\mathrm{ii}}_1^m+{\mathrm{ii}}_4^m)-({\mathrm{ii}}_2^m+{\mathrm{ii}}_3^m)]-[({\mathrm{ii}}_{1^{\&prime;}}^m+{\mathrm{ii}}_{4^{\&prime;}}^m)-({\mathrm{ii}}_{2^{\&prime;}}^m+{\mathrm{ii}}_{3^{\&prime;}}^m)\left]\right\}$

In the formula,

Be the pixel value of summit p place m-rank integral image, p=1,2,3,4,1 ', 2 ', 3 ', 4 ';

Corresponding to pixel value sum in the whole moving window,

Be pixel value sum in the protecting window;

Step 23: with 2-rank, 4-rank sample moment substitution formula (1) and (2), can try to achieve G by simple operation ⁰The statistical parameter of model.

4. according to claim 1 to 3 each described a kind of quick A CCA-CFAR SAR image object detection methods based on integral image, it is characterized in that: described by the quick A CCA-CFAR algorithm based on integral image, realize that the SAR image object detects, its specific implementation step is as follows:

Step 301: adopt based on G ⁰The global threshold CFAR algorithm that distributes carries out the target pre-segmentation, generates the target index matrix; For the ease of follow-up computing, to this index matrix negate, obtain the background pixel index matrix, wherein background dot is 1, impact point is 0, is used for marking all candidate background pixels;

Step 302: by with background pixel index matrix and original image dot product, remove the interference of candidate target pixel, obtain the background clutter image, namely with the reservation background pixel value of original image equidimension, the image of target pixel value zero setting;

Step 303: the m-rank integral image that calculates the background clutter image;

Step 304: invariable false alerting and moving window parameter are set, and wherein the size of protecting window should be greater than the size of target to be detected, and sliding window size should participate in the statistical parameter estimation to ensure abundant background pixel fully greatly;

Step 305: utilize moving window to pursue the picture element scan original image, and by m-rank integral image and background pixel index matrix, calculate the m-rank sample moment of background pixel;

Step 306: utilize the m-rank sample moment of background pixel, adopt the MoM method to estimate G ⁰The statistical parameter that distributes;

Step 307: utilize statistical parameter to calculate the CA-CFAR local threshold;

Step 308: by the size of compare test unit and local threshold, judge that whether this unit is the candidate target pixel, is to be 1, otherwise is 0;

Step 309: judge whether to continue scanning, if do not scan complete width of cloth image, then skip to step 305, scan next test cell; Otherwise, carry out next step;

Step 310: remove false-alarm by subsequent treatment, merge the object pixel zone, thereby obtain final target detection result.

5. a kind of quick A CCA-CFAR SAR image object detection method based on integral image according to claim 4 is characterized in that: the subsequent treatment described in the step 310 is processed or the target area cluster for counting wave filter, morphology.

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