CN103065320A - Synthetic aperture radar (SAR) image change detection method based on constant false alarm threshold value - Google Patents

Abstract

The invention discloses a synthetic aperture radar (SAR) image change detection method based on a constant false alarm threshold value. The SAR image change detection method based on the constant false alarm threshold value mainly resolves the problem that a corresponding relationship between a false alarm and a threshold value is not clear in an existing SAR image change detection. The realization process includes the following steps: (1) aimed at the input two amplitude SAR pictures, calculating a ratio image based on block-means; (2) according to an expected false-alarm probability, determining a corresponding threshold value based on a ratio distribution model; (3) according to the threshold value, segmenting the ratio image so as to gain a change detection result image. The SAR image change detection method based on the constant false alarm threshold value can calculate the corresponding threshold value according to the preset false-alarm probability, so that the SAR image change detection result in accordance with the expected false-alarm probability can be gained. Meanwhile, due to the consideration of neighborhood information, the noise interference is reduced and the detection precision is improved. The SAR image change detection method based on the constant false alarm threshold value can be used for resource and environment monitoring, agricultural research, and natural disaster monitoring and evaluation.

Description

SAR image change detection method based on the CFAR threshold value

Technical field

The invention belongs to technical field of image processing, relate to a kind of SAR image change detection method based on the CFAR threshold value, can be used for interested feature changes information in the SAR image is carried out Check processing.

Background technology

The SAR Image Change Detection is intended to study Same Scene, two width of cloth of different periods or the difference between several SAR images and obtains interested feature changes information.Because the SAR system has round-the-clock, round-the-clock, high resolving power and the powerful unique advantages such as penetration capacity, has simultaneously the fixing heavily visit cycle, so it is more suitable for detecting in changing than remote optical sensing, is well to change to detect information source.SAR Image Change Detection technology is widely used in a lot of aspects, such as resources and environment monitoring, agricultural research, Natural calamity monitoring and assessment, military analysis etc.

SAR Image Change Detection technology is one of important application of sensor information process field, it is by the difference of spectral signature difference or space structure characteristic between the remote sensing images of analyzing the shooting of areal different times, obtains the transformation of the needed type of ground objects of people or the variation of interior condition and state.Change detection techniques can detect the variation between different time gradation of image value or the local grain, and the target that to need on this basis is in shape, position, quantity, and the situation of change of other attributes.But the impact of SAR Image Speckle noise still is a Main Bottleneck of its application.

Through the development of nearly more than ten years, for dissimilar remote sensing images, scholars have proposed technology and the method that many variations detect, can be rough classify as: 1: relative method after the classification; 2: based on the simple operation method; 3: the image-based modeling method; 4: based on the space field characteristic method; 5: based on change detecting method of feature etc.Wherein, change detection algorithm image difference method, ratioing technigue, average ratioing technigue comparatively commonly used can range the simple operation method in recent years.Differential technique: its main process is 2 o'clock phase SAR image corresponding pixel points gray-scale values to be subtracted each other obtain disparity map, then gets 0 ~ 255 threshold value and cuts apart, and obtains changing and indeclinable zone; The image ratio method: its main process is the ratio difference figure that calculates 2 o'clock phase SAR image corresponding pixel points gray-scale values, if pixel does not change, then ratio should be close to 1, on the contrary then much smaller than or greater than 1; The average ratioing technigue: its main process be use 2 o'clock phase SAR image corresponding pixel points with and the ratio of the average of neighborhood territory pixel point obtain disparity map, the method and ratioing technigue are similar.The differential technique algorithm is simple, but is subject to the impact of the objective condition such as SAR image quality, easily produces " the pseudo-variation " information; Ratioing technigue and average ratioing technigue, all insensitive to the multiplicative noise of SAR image, therefore be mainly used in the SAR image of multiplicative noise.

Method in sum, although aspect a lot of, all obtained application, but still exist many problems: mutual corresponding model between neither one false-alarm and the threshold value in (1) traditional method can't obtain the false-alarm probability of testing result in the practical application;

(2) do not consider neighborhood information based on the method for point, be subjected to noise large, accuracy of detection is not high.

Summary of the invention

The object of the invention is to the deficiency for above-mentioned prior art, propose a kind of SAR image change detection method based on the CFAR threshold value, to adjust false-alarm probability, practical requirement; Noise decrease disturbs, and improves accuracy of detection.

The technical scheme that realizes the object of the invention is: the ratio according to speckle noise distributes, and the correlative value new probability formula is derived, thereby obtains the relational expression about false alarm rate and threshold value.By given false alarm rate definite threshold, and satisfy different actual demands by adjusting false alarm rate, concrete steps comprise as follows:

(1) the two secondary amplitude SAR image V of L of being are looked several in input ₁And V ₂, respectively with magnitude image V ₁, V ₂In pixel v _{1, k}, v _{2, k}Centered by, get the neighborhood piece u of M * M size _{1, k}, u _{2, k}, calculate piece average ratio r corresponding to per two pixels _kThereby, obtain ratio figure R;

(2) determine the threshold value T that desired false-alarm probability α is corresponding;

2a) calculate each ratio r according to following ratio distribution probability formula _kProbability p (the r that occurs _k):

$p\left(r_k\right)=\frac{2\mathrm{\Γ}\left(\mathrm{NL}\right)}{{\mathrm{\Γ}}^2\left(\mathrm{NL}\right)}\frac{{2r}_k^{2\mathrm{NL}-1}}{{(r_k^2+1)}^{2\mathrm{NL}}},$ r _k∈[0,1]

Wherein, Γ () expression gamma function, points N=M * M, NL represent points N and the product of looking several L in the piece;

2b) according to Probability p (r _k), obtain ratio probability function P (T):

$P\left(T\right)={\∫}_0^{T}p\left(r_k\right)d{r}_k$

Wherein, T is threshold value;

2c) according to ratio probability function P (T), obtain false-alarm probability α by probability statistics character:

$\mathrm{\α}=\frac{{\∫}_0^{T}p\left(r_k\right)d{r}_k}{{\∫}_0^{1}p\left(r_k\right)d{r}_k}$

Following formula is carried out the linear expression that abbreviation obtains T and α:

α＝bT

Wherein constant b satisfies relational expression:

$\mathrm{bT}=\frac{{\∫}_0^T\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}}{{\∫}_0^1\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}}$

And NL is by step 2a) try to achieve;

2d) again according to given false-alarm probability α, calculate needed threshold value T:

$T=\frac{\mathrm{\α}}{b}$

(3) according to the threshold value T that tries to achieve, travel through whole ratio images R, pixel value among the R is set to 255 less than the point of threshold value T, pixel value is set to 0 more than or equal to the point of threshold value T, thereby obtains changing testing result figure.

The present invention compared with prior art has following advantage:

1. the present invention can carry out in the spatial domain, and implementation procedure is simple, and can Parallel Implementation;

2. the present invention is by the false alarm rate definite threshold, and then obtains testing result by determined threshold value, can satisfy different actual demands, and testing result more has practical value, and is more flexible;

3. the present invention has considered neighborhood information, has reduced noise, and testing result is more stable, and precision is higher.

Description of drawings

Fig. 1 is process flow diagram of the present invention;

Fig. 2 is two lena original graph and standard drawings to be synthesized that emulation of the present invention is used;

Fig. 3 is the as a result figure that the SAR image that synthesizes based on Fig. 2 is changed detection with the inventive method;

Fig. 4 is the true SAR striograph of two width of cloth Ottawa that emulation of the present invention is used;

Fig. 5 is to change the as a result figure of detection based on Fig. 4 with the inventive method;

Fig. 6 is two box original graph and standard drawings to be synthesized that emulation of the present invention is used;

Fig. 7 is the as a result figure that the SAR image that synthesizes based on Fig. 6 is changed detection with the inventive method.

Embodiment

With reference to Fig. 1, the present invention includes following performing step:

Step 1, input are looked number and are the two secondary amplitude SAR image V of L ₁And V ₂, calculate piece average ratio r corresponding to per two pixels _k, obtain ratio figure R.

1a) respectively with pixel v _{1, k}, v _{2, k}Centered by, respectively get the field piece u of M * M size _{1, k}, u _{2, k}

1b) calculate respectively above-mentioned u _{1, k}, u _{2, k}Average

Be called the piece average; Wherein,

${\stackrel{\‾}{v}}_{1,k}=\mathrm{mean}\left(u_{1,k}\right),$

${\stackrel{\‾}{v}}_{2,k}=\mathrm{mean}\left(u_{2,k}\right)$

In the formula, mean () represents mean value function;

1c) calculate above-mentioned average Ratio r _k:

$r_k=\min \{\frac{{\stackrel{\‾}{v}}_{1,k}}{{\stackrel{\‾}{v}}_{2,k}},\frac{{\stackrel{\‾}{v}}_{2,k}}{{\stackrel{\‾}{v}}_{1,k}}\},$ r _k∈[0,1]；

1d) with above-mentioned ratio r _kValue as k pixel among the ratio images R.

Step 2 is determined the threshold value T that desired false-alarm probability α is corresponding;

2a) calculate each ratio r according to following ratio distribution probability formula _kProbability p (the r that occurs _k):

$p\left(r_k\right)=\frac{2\mathrm{\Γ}\left(\mathrm{NL}\right)}{{\mathrm{\Γ}}^2\left(\mathrm{NL}\right)}\frac{{2r}_k^{2\mathrm{NL}-1}}{{(r_k^2+1)}^{2\mathrm{NL}}},$ r _k∈[0,1]

Wherein, Γ () expression gamma function, points N=M * M, NL=N * L represent points N and the product of looking several L in the piece;

2b) according to Probability p (r _k), obtain ratio probability function P (T):

$P\left(T\right)={\∫}_0^{T}p\left(r_k\right)d{r}_k,$

Wherein, T is threshold value;

2c) according to ratio probability function P (T), obtain false-alarm probability α by probability statistics character:

$\mathrm{\α}=\frac{{\∫}_0^{T}p\left(r_k\right)d{r}_k}{{\∫}_0^{1}p\left(r_k\right)d{r}_k},$

Following formula is simplified the linear expression that obtains T and α:

α＝bT，

Wherein constant b satisfies relational expression:

$\mathrm{bT}=\frac{{\∫}_0^T\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}}{{\∫}_0^1\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}},$

And NL is by step 2a) try to achieve;

2d) according to given false-alarm probability α, calculate needed threshold value T:

$T=\frac{\mathrm{\α}}{b},$

Step 3, according to the threshold value T that tries to achieve, structure changes testing result figure:

Utilize this threshold value T to travel through whole ratio images R, ratio among the R is made as 255 less than the point of threshold value T, ratio is made as 0 more than or equal to the point of threshold value T, and then obtains changing testing result figure.

Effect of the present invention can further confirm by following experiment:

One. experiment condition and content

Experiment condition: it is as follows to test employed emulating image:

Test emulation image lena as described in Figure 2, wherein Fig. 2 (a) and 2 (b) are lena figure to be synthesized, and Fig. 2 (c) is examination criteria figure;

Based on Fig. 2 (a) and the synthetic SAR image of looking several L=1 of 2 (b), as described in Fig. 3 (a);

True picture Ottawa as described in Figure 4, wherein Fig. 4 (a) represents in May, 1997 this area's geomorphology information, Fig. 4 (b) expression in August, 1997 this area's geomorphology information;

Test emulation image box as described in Figure 6, wherein Fig. 6 (a) and 6 (b) are box figure to be synthesized, and Fig. 6 (c) is examination criteria figure;

Based on Fig. 6 (a) and the synthetic SAR image of looking several L=1 of 6 (b), as described in Fig. 7 (a).

Experiment content: under above-mentioned experiment condition, use the inventive method that synthetic SAR image 3 (a) and 7 (a), true SAR image 4 are detected, all experiments are all carried out under the hypothesis of homogeneous region.

Two. experimental result

Experiment one: use the inventive method that Fig. 3 (a) is processed, wherein block size gets respectively 3 * 3,5 * 5, and the employing false alarm rate is that threshold value and the false alarm rate of 0.001 correspondence is the threshold value of 0.0015 correspondence.Laboratory test results is shown in Fig. 3 (b), 3 (c), and wherein Fig. 3 (b) and Fig. 3 (c) represent respectively that from left to right block size is 3 * 3,5 * 5 testing result.

By Fig. 3 (b) and Fig. 3 (c) as seen, for given false alarm rate, the inventive method can effectively detect the changing unit of synthetic SAR image; And, in the constant situation of false alarm rate, the piece that adopts less, assorted some piece of detection is less, under piece became large situation, though assorted point tails off, assorted some piece became large, is unfavorable for effective detection.

Experiment two: use the inventive method that Fig. 4 is processed, wherein block size gets respectively 3 * 3,5 * 5, and the employing false alarm rate is that threshold value and the false alarm rate of 0.001 correspondence is the threshold value of 0.0015 correspondence.Laboratory test results as shown in Figure 5, wherein Fig. 5 (a) and 5 (b) represent respectively that from left to right block size is 3 * 3,5 * 5 testing result.

As seen from Figure 5, for given false alarm rate, the inventive method can effectively detect the changing unit of true SAR image.

Experiment three: use the method for the inventive method and existing point that Fig. 7 (a) is processed, the false alarm rate that the inventive method adopts is 0.001, and block size is 3 * 3.Laboratory test results shown in Fig. 7 (b), wherein Fig. 7 (b) from left to right be respectively a little method and the testing result of the inventive method.

As a result figure by 7 (b) can find out, can not effectively detect region of variation based on the method for putting, and the inventive method can detect the zone of variation preferably.

Experiment four: use the inventive method to determine different points N, look several L, the corresponding threshold value of false alarm rate, and Fig. 3 (a) is changed detection, testing result sees Table 1; What use the inventive method obtained changes detection based on point and block-based method, and the testing result contrast sees Table 2.

The different points N of table 1, the experiment of looking several L and the contrast of theoretical false alarm rate

Table 2 the inventive method and based on the testing result contrast of the method for point

The result of table 1 and table 2 is under the hypothesis of homogeneous region and obtains.

As can be seen from Table 1, putting under the said conditions together with looking number, the difference of two false alarm rates is compared and is substantially met, and this and theoretical analysis meet.Although experiment value and theoretical value have certain error, in the fluctuation range that allows.This is because when calculating ratio based on the piece average, has the pixel with the central pixel point non-homogeneous in the piece, causes the calculating of thresholding to have certain error.This error becomes large with the size increase of piece.The present invention uses less piece size, and for example 3 * 3.

As can be seen from Table 2, with based on the point method compare, the inventive method has been considered neighborhood information, has reduced noise, testing result is more stable, precision is higher.

The variation of adopting method proposed by the invention to carry out the SAR image under above experiment condition detects.Realize and can find out that compare with existing threshold transformation detection method, use the inventive method is faster easy, and can satisfy different actual demands from algorithm, and testing result has practical value more, more flexible.

Claims (2)

1. the SAR image change detection method based on the CFAR threshold value comprises the steps:

(1) the two secondary amplitude SAR image V of L of being are looked several in input ₁And V ₂, respectively with magnitude image V ₁, V ₂In pixel v _{1, k}, v _{2, k}Centered by, get the neighborhood piece u of M * M size _{1, k}, u _{2, k}, calculate piece average ratio r corresponding to per two pixels _k, obtain ratio figure R;

(2) determine the threshold value T that desired false-alarm probability α is corresponding;

2a) calculate each ratio r according to following ratio distribution probability formula _kProbability p (the r that occurs _k):

$p\left(r_k\right)=\frac{2\mathrm{\Γ}\left(\mathrm{NL}\right)}{{\mathrm{\Γ}}^2\left(\mathrm{NL}\right)}\frac{{2r}_k^{2\mathrm{NL}-1}}{{(r_k^2+1)}^{2\mathrm{NL}}},$ r _k∈[0,1]

Wherein, Γ () expression gamma function, points N=M * M, NL represent points N and the product of looking several L in the piece;

2b) according to Probability p (r _k), obtain ratio probability function P (T):

$P\left(T\right)={\∫}_0^{T}p\left(r_k\right)d{r}_k$

Wherein, T is threshold value;

2c) according to ratio probability function P (T), obtain false-alarm probability α by probability statistics character:

$\mathrm{\α}=\frac{{\∫}_0^{T}p\left(r_k\right)d{r}_k}{{\∫}_0^{1}p\left(r_k\right)d{r}_k}$

Following formula is carried out the linear expression that abbreviation obtains T and α:

α＝bT

Wherein constant b satisfies relational expression:

$\mathrm{bT}=\frac{{\∫}_0^T\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}}{{\∫}_0^1\frac{r_k^{2\mathrm{NL}-1}d{r}_k}{{(r_k^2+1)}^{2\mathrm{NL}}}}$

And NL is by step 2a) try to achieve;

2d) again according to given false-alarm probability α, calculate needed threshold value T:

$T=\frac{\mathrm{\α}}{b}$

(3) according to the threshold value T that tries to achieve, travel through whole ratio images R, pixel value among the R is set to 255 less than the point of threshold value T, pixel value is set to 0 more than or equal to the point of threshold value T, thereby obtains changing testing result figure.

2. the SAR image change detection method based on the CFAR threshold value according to claim 1 is characterized in that piece average ratio r corresponding to per two pixels of the described calculating of step (1) _k, carry out in accordance with the following steps:

2a) respectively with pixel v _{1, k}, v _{2, k}Centered by, respectively get the neighborhood piece u of M * M size _{1, k}, u _{2, k}

2b) calculate respectively above-mentioned neighborhood piece u _{1, k}, u _{2, k}Average

Be called the piece average; Wherein,

${\stackrel{\‾}{v}}_{1,k}=\mathrm{mean}\left(u_{1,k}\right),$ ${\stackrel{\‾}{v}}_{2,k}=\mathrm{mean}\left(u_{2,k}\right),$

Wherein, mean () expression mean value function;

2c) calculate above-mentioned average

Ratio r _k:

$r_k=\min \{\frac{{\stackrel{\‾}{v}}_{1,k}}{{\stackrel{\‾}{v}}_{2,k}},\frac{{\stackrel{\‾}{v}}_{2,k}}{{\stackrel{\‾}{v}}_{1,k}}\},$ r _k∈[0,1]

2d) with above-mentioned ratio r _kValue as k pixel among the ratio images R.

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