基于小面元的多源遥感影像高精度配准方法（High accuracy registration method for multi-source remote sensing image based on small surface element）

1、基于小面元的多源遥感影像高精度配准方法（High accuracy registration method for multi-source remote sensing image based on small surface element）Article no. : 1009-427x (2003) 02-0124-05High accuracy registration method for multi-source remote sensing image based on small surface elementXing shuai, tan bing, li jiansheng

2、, xu qing, geng xun(school of surveying and mapping, university of information engineering, zhengzhou, henan, 450052)Abstract: based on several high accuracy matching methods, the idea of small face value differential correction is introduced, and a method of high precision image relative registrati

3、on is designed. realThe results show that this method has a strong adaptability to multiple remote sensing images, and the relative accuracy is high, especially for the obvious or multi-source image in the mountainous areaHigh precision registration.Key words: multi-source remote sensing image; High

4、 accuracy registration; Matching; Minor correctionMiddle image classification no. : TP751 literature identification code: AMulti-source remote sensing satellite images refer to different electromagnetic waves.Different time phase, different incident Angle, different imaging mechanism, noThe spatial

5、resolution of the same region is different from that of the same area.But you can complement each other 1. We are just making use of its letterThe characteristics of mutual complementarity and cooperation, through certain treatment methods, such as meltingThe combination, classification, change dete

6、ction, identification, etc., extract useful or IThese methods are applied to the information of interestPremise and foundation. Especially high accuracy registration has become a shadowLike the necessary conditions for obtaining accurate information, meanwhile, high precision image matchingTime chan

7、ge detection, stereo matching, motion analysis and graphBasic 2 applications such as sequence analysis.1. Overview of image registration algorithmAt present, the method of image registration is mainly divided into space domain and frequencyThere are two broad categories. The spatial domain method on

8、ly from the geometric deformation and ash of the imageThe Angle of degree difference is simple and easy to realize, and the ordinary visible lightImage and partial infrared image and radar image can be processedIt has the advantages of high precision and fast speed. In most literatureThe methods pro

9、posed are all in this category, such as correlation function method and dynamic regulationMethod, integral method, least squares 3, and Ton Jezching,The point of the corresponding relationship between the two image regions is proposed by Jain A KThe matching method 4, zhou jie and others based on th

10、e direction of small porterMatch method 5, etc. The frequency domain method is involved in the spatial domainThe frequency domain conversion, therefore, is more complex. Although it can be achievedIt is more accurate, but its geometrical deformation and radiation deformity of the two imagesThere are

11、 higher requirements and narrower applications, mostly for researchWork. There are some references to this method, such as Harold SThe pre - filtering and Fourier transform detection is proposed by StoneMethod to match the image of visible light and near-infrared image with 6,Hassan Shekarforoush et

12、 al through the cross power spectrum of the imageThe multi-phase decomposition is estimated to reach the sub-pixel level with 2.The constant presence of various registration methods makes the accuracy of the registration more accurateThe higher it is, from pixel to sub-pixel, all the way to sub-pixe

13、l level.But there is always a problem, which is one methodSeveral groups or images can have good effects, but they are notThere should be other types of data, so we always hope to find oneHigh degree of automation and strong adaptabilityMethod 7.Some traditional methods, though, have been found to b

14、e oneThe limitations of localization, but have a strong adaptability, such as correlation function methodBoth visible and radar images can be processed. Due to theHere, we cite several traditional matching methods and image pyramidsMatching strategy and introducing the principle of small triangular

15、surface element differential correction,A novel multi-source remote sensing image with high accuracy is designedMethod.2. High precision registration method based on the remote sensing image of small surface elementPut forwardThe main idea of this method is to extract it on the reference imageRegist

16、ration Control Point,RCP, by matching the point-to-point of the same name, based on the small surface elementThe accurate registration of the image is corrected.The basic process of this method is: in the reference image pyramidSparse feature points are extracted at the highest level according to ce

17、rtain criteriaRCP, layer by layer matching to the lowest layer (original image layer)Basic control points; Then the dense RCP is extracted at the lowest levelLine correlation coefficient matching, in target image (i.e.To obtain the corresponding image points of the same name; And then we're goin

18、g to get rid of some errorsThe point pair, and the unreliability of the point to the whole relaxation methodMatch, and then match the least squares to all the pointsTo improve the accuracy; Finally, the same points of the same name form a dense threeDate of collection: 2002-11-25; Date of repair: 20

19、03-03-11The author profile: xing shuai (1979 -), male, henan xinyang, master degree, research direction for photogrammetry and remote sensing.Volume 20, phase 2In June 2003,Journal of surveying and mapping universityThe Journal of Surveying and MappingVol. 20, No. 2Jun. 2003 corner net, in the corre

20、sponding triangle network, carry out the differential of each small elementCorrection to achieve accurate image registration. The logical flow of this methodAs shown in figure 1.FIG. 1 logical flow diagram of high-precision registration methodThe image pretreatment here mainly consists of two aspect

21、s: 1Radiation correction, such as gray stretch, histogram adjustment, color order adjustment, lightAdjustment of degree and contrast. The matching of feature points is mainly returnedIt's the grayscale distribution of the peak at some point and the surrounding point, rightThe grayscale distribut

22、ion of a peak and its surrounding points should be found on the imageThe former is the same point as the former.Therefore, the difference of the radiation characteristics of the image will lead to the ashes between the points of the same nameThe difference of degree characteristic, then cause mismat

23、ching. Two is geometric correction,Such as image rotation, translation, scale scaling, etc. Between two pointsThe degree of relevancy is in the window, and the same is guaranteedThe smaller two Windows contain the same range of target areas as the sameIf it is necessary, it loses its comparability,

24、and there is no way to talk about itUp.This method mainly has the following four characteristics:1) high reliability. The feature points are used as RCPConsider the difference between the feature points and the surrounding grayscale, in the graphLike the main manifestations of the road intersection

25、point, the corner of the house, the riverTurn the corner and so on, this is to improve the match accuracy and the reliability all have veryGreat help.2) high accuracy. By complementing each other, several traditional pieces are playedThe advantages of the method,Make up for the lack of a single meth

26、od. The correlation coefficientThe accuracy of the method is not high, but the speed can be used as a rough match. Overall the pineAlthough the accuracy is not high, the speed is not fast, but can improve the resultReliability; The least square method can achieve high accuracy, but it needs to beThe

27、 two sides provide a more precise initial point. We focused on the threeAdvantage, with a high accuracy of 0.1 pixel.3) overall optimality. Use the differential of the small triangular surface elementCorrection, overcome the existence of a single polynomial correction in the imageThe local registrat

28、ion is incorrect and the image edge deformation is guaranteedThe overall optimization of the image is corrected and the geometry is greatly improvedThe image registration accuracy of remote sensing image is greatly deformed.4) strong adaptability. There are two aspects: one is to the various classes

29、Strong adaptability of remote sensing image, including visible light and near redAll kinds of remote sensing images, such as external and hyperspectral images, can be enteredHigh precision registration; The second is the strong adaptability to different terrain areas.Whether flat or hilly, you can a

30、chieve high precisionDegree of registration.Analysis of key technologies3.1 extraction of feature pointsThe feature is something or something like the value of a dollar relative to its neighborhoodA kind of structural property that is generally able to shift well,Rotation invariance. Therefore, the

31、feature matching can be higherReliability of 8. The point feature is the most basic characteristic, but it isThe scope is the most extensive, and its calculations and descriptions are simple,Therefore, the feature points are selected as the matching points. Extract feature pointsWe're going to u

32、se what we call an interest operator or an advantage operator, a common interestOperator has Moravec operator, Hannah operator, DreschlerChildren and Forstner operators. In the literature 3, ForstnerThe precision and speed of the operator are relatively moderate, so this calculation should be usedSu

33、bextract feature points.3.2 the coarse matching3.2.1 correlation coefficient matchingCreate a rectangular window for the feature points on the reference imageThe target window, like m by n size, is selected on the target imageSelect a larger area window as the search window and create oneAn m by n s

34、ize matching window is pixel by pixel on the search windowMove, and compare the target window with the matching windowThe correlation coefficient is the most similar to the target windowThe center is the image point of the same name. The correlation coefficients are calculated based on the following

35、 formula:Rho = (mI = 1 nJ = 1(g '(I, j) - g') (g (I, j) - "g") /mI = 1 nJ = 1(g '(I, j) - g'), 2mI = 1 nJ = 1(g (I, j) - "g") 2In the literature 9, the correlation coefficient threshold is determined to matchAn important indicator of reliability, and its selection

36、 and matching windowThe size is closely related. In practice, correlation coefficients are based on twoA random signal is estimated, so it obeys a certain probability distribution.A random sample of correlation coefficients is also presented in the 9 literaturefunctionW = 12 ln (1 + r1 - r)The w her

37、e is approximately the normal distribution, so you get the confidence systemThe high accuracy registration method of multi-source remote sensing image based on small surface element is 0.The range of correlation coefficients at 95- 1 + 1 < < r y y y y - 1 + 1 (1)Type in theY = 1 + rho 1 - rho

38、e3.92 / N - 3Where, N is the size of the matching window; Rho is the correlation coefficient threshold valueCount value. The (1) type can be adapted to the size of the matching windowThe correlation coefficient threshold is determined.To improve reliability, we built the image pyramidTake the RCP of

39、 the upper layer as the lower layerControl point, reduce search scope, improve the success rate of matching.3.2.2 the whole relaxation method matchesAfter you get the correlation coefficient threshold, for those relationshipsThe number of matching points below the threshold is matched to the overall

40、 relaxation methodAchieve higher reliability results. Relaxation is the best solution for the wholeOne of the ways that it USES contextual information within the neighborhood is consideredIs the binding and consistency between objects and is calculated by iterationFinally get the most consistent and

41、 compatible results of 9, specificSee the literature 3, 9. You can get it back by relaxationThese correlation coefficients are less than the threshold matching points and the new matching pointsThe reliability of the original results is improved obviously. This stepProcessing is to provide reliabili

42、ty for the next least square matchA strong set of values.3.3 high-precision matching - -least squares matchThe least square image match was developed in the 1980sUp. The method makes full use of the letter in the image windowThe interest rate is calculated to make the image match up to 1/10 or even1

43、/100 pixels high precision. To this end, the least squares image match is givenIt is called "high precision image matching", and the specific algorithm is seen in the literature 3, 9.Therefore, the method can ensure the accuracy of matching results0.1 pixels above.The least squares image m

44、atching requires attention to convergenceThe topic. In the matching iterative calculation, the speed of convergence is mainly determinedAccuracy of initial value. The more accurate the initial value, the faster it converges. thoseThe points that have been iteratively unconvergent will be removed and

45、 not involvedCorrection of the surface to ensure the reliability of the results.3.4 differential correction of small triangular surface elementThe final result of image registration is generated based on the target imageAn image with a geometric alignment of the reference image, which is the rootThe

46、 geometry of the reference image and the target image is calculatedTo recorrect the target image to obtain the registration image. The current commonly usedThe method is polynomial, which is represented by a high degree polynomialThe geometric relationship between images is then calculated through R

47、CPThe item type is corrected by this polynomial. But for some differencesThe image of the sensor, because the geometric distortion between them is often notOften complex, especially in mountain view, with a single, even if highPolynomials cannot be described at all, and the accuracy of the registrat

48、ion is not high.Since the feature points are used as RCP, the feature point is oneIt is concentrated in the feature line and sparse in the flat regionsThe characteristics of irregular triangle mesh are consistent. It can be completely tunedThe whole feature point is controlled by the extraction thre

49、shold, local addition and delete feature pointsThe distribution of RCP, triangulation of the whole image, and the fluctuationLarge areas build dense triangulation nets,And the flat regionSparse triangulation, and then each triangular surface elementCorrect. Based on this idea, a large number of inte

50、nsive RCP was establishedTriangle mesh, then create one for each small triangular face elementpolynomialX is equal to a0 plus a1x plus a2yY is equal to b0 plus b1 times x plus b2yBased on the coefficients a0, a1,A2 and b0, b1, b2, and then follow this polynomial to the target imageThe triangle is co

51、rrected to the triangle on the reference image. Experiments show thatThe area of each small triangle is small, and its internal deformation is completeYou can describe it in terms of a single polynomial, and you don't need to use a higher orderPolynomials can also work well. This method makes ge

52、ometryThe problem of registration of complex remote sensing images is effectiveThe solution.One of the key techniques in this approach is to build a triangulation network.The complete and reliable triangulation network is the basis for small - surface differential correctionIf the construction of a

53、triangulation network error, the result will be correctedIt's not right, but the speed of the triangulation also determines the whole thingRegistration efficiency. The commonly used method of constructing triangulation network has Angle judgmentBroken law, thic-ssen polygons and DelaunayTriangle

54、 network 3. The latter is a triangulation that is constructed by any triangleIt is unique that the experiment selected this method, 431 RCPThe resulting 847 triangles took less than 2 min. Structural network processIn the middle, each RCP participates in the construction network, without crossing or

55、 splittingIt's not normal. Figure 2 shows the network results.FIG. 2 triangle network composed of RCPIn some special cases, such as the height change in the imageThe sharp area is used by the same namesake that was initially obtained in the areaIn 2003, the journal of surveying and mapping insti

56、tute of surveying and mapping could not be corrected with high accuracy, so it could be suitable in this areaWhen adding some feature points and forming the same name, increase the areaThe density of the small triangular surface element, the area of each triangular surface elementBe as small as possible until you meet the precision requirements.4 experimental situation1) experiment 1, the registration of the image of flat area is integratedSelect the SPOT panchromatic image of an urban area with TM 7, 4, 2