CN103077499B - SAR (Synthetic Aperture Radar) image pre-processing method based on similar block - Google Patents
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Abstract
本发明公开了一种基于相似块的SAR图像预处理方法,主要克服现有方法不能保持图像纹理细节和压缩强反射点目标的问题。其实现过程是:(1)对输入的SAR图像,并计算图像的方差系数矩阵;(2)利用方差系数,将图像像素点分为平滑区域和纹理细节区域;(3)对平滑区域采用均值滤波得到处理后的像素值;(4)对纹理细节区域,采用旋转操作;(5)依据比值概率分布得到相似性度量公式,并计算待处理点与其8邻域像素点的相似性;(6)取待处理点和与它最相似的3个点的均值作为该像素点的灰度值。(7)计算出所有像素点的恢复值,得到预处理图像。本发明在抑制噪声的同时能够更好的保护图像的纹理和结构信息,能很好的保持强反射点目标,可用于图像应用前的预处理。
The invention discloses a SAR image preprocessing method based on similar blocks, which mainly overcomes the problems that the existing methods cannot maintain image texture details and compress strong reflection point targets. The implementation process is: (1) For the input SAR image, calculate the variance coefficient matrix of the image; (2) Use the variance coefficient to divide the image pixels into a smooth area and a texture detail area; (3) Use the mean value for the smooth area Filter to obtain the processed pixel value; (4) Rotate the texture detail area; (5) Obtain the similarity measurement formula according to the ratio probability distribution, and calculate the similarity between the point to be processed and its 8 neighboring pixels; (6 ) Take the average value of the point to be processed and the three points most similar to it as the gray value of the pixel. (7) Calculate the restored values of all pixels to obtain the preprocessed image. The invention can better protect the texture and structure information of the image while suppressing the noise, can well keep the strong reflection point target, and can be used for the preprocessing before the application of the image.
Description
技术领域technical field
本发明属于图像处理技术领域,涉及一种基于同质相似块的SAR图像预处理方法,可用于SAR图像应用前的预处理。The invention belongs to the technical field of image processing, and relates to a SAR image preprocessing method based on homogeneous and similar blocks, which can be used for preprocessing before the application of the SAR image.
背景技术Background technique
合成孔径雷达SAR所成的图像具有全天候、全天时、高分辨率和强大的穿透能力等特点,因此,这种图像被广泛的应用到目标识别、变换检测和水面监视等领域。然而,SAR图像易被乘性噪声所腐蚀,这种噪声来自后向散射雷达反射的连续干扰,这种斑点噪声毁坏了SAR图像辐射测量的分辨率,同时影响到背景分析的性能和理解任务。The image formed by synthetic aperture radar SAR has the characteristics of all-weather, all-time, high resolution and strong penetrating ability. Therefore, this image is widely used in the fields of target recognition, transformation detection and water surface surveillance. However, SAR images are easily corrupted by multiplicative noise, which comes from the continuous interference of backscattered radar reflections. This speckle noise destroys the radiometric resolution of SAR images and affects the performance of background analysis and understanding tasks.
随着SAR图像在军事和民用方面得到广泛应用,SAR图像处理成为SAR技术的又一个研究重点。SAR图像中包含了大量由于成像界面上散射点的相干回波随机干涉而造成的相干斑,这些斑点噪声会极大降低图像分割、边缘检测、特征提取、目标识别和其它信息处理技术的有效性,使得SAR图像处理不能达到预期目标。因此,对SAR图像的处理前的预处理是一个不可缺少的过程。With the widespread application of SAR images in military and civilian applications, SAR image processing has become another research focus of SAR technology. SAR images contain a large number of coherent spots caused by the random interference of coherent echoes from scattering points on the imaging interface, and these speckle noises will greatly reduce the effectiveness of image segmentation, edge detection, feature extraction, target recognition and other information processing techniques , so that the SAR image processing cannot achieve the expected goal. Therefore, the preprocessing before processing the SAR image is an indispensable process.
预处理的目的就是在抑制图像噪声的同时保留图像的特征信息,像图像纹理,边缘和点状目标等。这和图像的去噪有很大的差别,去噪是要尽可能的去除噪声,这样就会过平滑图像的纹理,图像结构信息如边缘、线性体、点等目标会在一定程度上被模糊或滤除,不利于后续图像的处理。目前常用的SAR图像预处理方法主要有两种:1)3×3块均值方法;2)3×3MMSE方法。3×3块均值方法得到的预处理图像有较好的同质区域平滑能力,但是强反射点目标的亮度被严重压缩,而且边缘也被很大程度上模糊;3×3MMSE方法能较好的保持同质区域平滑能力,同样会压缩强反射点目标的亮度,图像的纹理细节变的模糊,不利于后续处理。The purpose of preprocessing is to suppress the image noise while retaining the feature information of the image, such as image texture, edge and point target. This is very different from image denoising. Denoising is to remove noise as much as possible, so that the texture of the image will be smoothed, and the image structure information such as edges, linear bodies, points and other objects will be blurred to a certain extent. Or filter out, which is not conducive to subsequent image processing. At present, there are mainly two kinds of SAR image preprocessing methods: 1) 3×3 block mean method; 2) 3×3MMSE method. The preprocessed image obtained by the 3×3 block mean method has a good smoothing ability in the homogeneous area, but the brightness of the strong reflection point target is severely compressed, and the edge is also largely blurred; the 3×3MMSE method can better Maintaining the smoothing ability of homogeneous areas will also compress the brightness of strong reflection point targets, and the texture details of the image will become blurred, which is not conducive to subsequent processing.
发明内容Contents of the invention
本发明的目的在于针对上述已有技术的不足,提出了一种基于同质相似块的SAR图像预处理方法,以使处理后的图像能够很好的保持同质区域的平滑能力,同时保护图像的强反射点目标和图像纹理结构信息,利于图像的后续处理。The purpose of the present invention is to address the deficiencies in the prior art above, and propose a SAR image preprocessing method based on homogeneous similar blocks, so that the processed image can well maintain the smoothness of the homogeneous region, while protecting the image The strong reflection point target and the image texture structure information are beneficial to the subsequent processing of the image.
实现本发明目的的技术关键是在用块计算相似性的时候,对图像块旋转,使相似块的相似性计算更准确;同时在同质相似块的基础上引进了比值分布概率,其技术方案包括如下步骤:The technical key to realize the object of the present invention is to rotate the image block when calculating the similarity with the block, so that the similarity calculation of the similar block is more accurate; at the same time, the ratio distribution probability is introduced on the basis of the homogeneous similar block, and its technical scheme Including the following steps:
(1)对于输入大小为(m,n)的L视SAR图像v,计算出所有像素点的方差系数CV,得到方差系数矩阵K;(1) For an L-view SAR image v with an input size of (m, n), calculate the variance coefficient CV of all pixels, and obtain the variance coefficient matrix K;
(2)设定方差系数分类阈值Tcv,对输入的SAR图像v进行分类,如果图像v中的像素点xi,j在方差系数矩阵K中的方差系数小于阈值Tcv,则执行步骤(3),否则执行步骤(4);(2) Set the variance coefficient classification threshold T cv to classify the input SAR image v, if the variance coefficient of the pixel point x i,j in the image v in the variance coefficient matrix K is less than the threshold T cv , then perform the step ( 3), otherwise execute step (4);
(3)将以该像素点xi,j为中心的3×3块内像素的均值作为该像素点预处理后的像素值;(3) The mean value of the pixels in the 3×3 block centered on the pixel point x i,j is used as the pixel value after preprocessing of the pixel point;
(4)对像素点xi,j的8邻域像素点xl,l=1,2,…,8,取以像素点xl为中心的3×3块vl,对块vl采取旋转操作,使以像素点xi,j为中心的3×3块vi,j和以像素点xl为中心的3×3块vl中同质的区域处在相同的位置,把vl旋转后的块记为;(4) For pixel x l in the 8 neighborhoods of pixel x i, j , l=1, 2,...,8, take a 3×3 block v l centered on pixel x l , and take The rotation operation makes the homogeneous area in the 3×3 block v i,j centered on the pixel point x i ,j and the homogeneous area in the 3×3 block v l centered on the pixel point x l be in the same position, and v l The rotated block is denoted as ;
(5)计算像素点xi,j与其8邻域像素点xl,l=1,2,…,8基于块比值概率的相似性距离:(5) Calculate the similarity distance between pixel x i,j and its 8 neighbor pixel x l , l=1,2,...,8 based on block ratio probability:
5a)取像素点xi,j为中心的3×3块vi,j,由步骤(4)得到其邻域像素点xl,l=1,2,…,8翻转后的的3×3块;5a) Take the 3×3 block v i,j centered on the pixel point x i,j , and obtain its neighborhood pixel point x l by step (4), where l=1,2,…,8 flipped 3× 3 blocks ;
5b)计算上述两个像素块vi,j和的比值ri,k:5b) Calculate the above two pixel blocks v i,j and The ratio r i,k of :
其中vi,k表示待处理像素点xi,j为中心的3×3块vi,j的第k个像素点的灰度值,vl,k表示像素点xl为中心的3×3块vl旋转后的块的第k个像素点的灰度值;Among them, v i,k represents the gray value of the kth pixel of the 3×3 block v i,j centered on the pixel point x i,j to be processed, and v l,k represents the 3× pixel point x l as the center 3 blocks v l rotated blocks The gray value of the kth pixel of ;
5c)利用比值分布概率公式计算出像素点xi,j的比值ri,k出现的概率p(ri,k);5c) Using the ratio distribution probability formula to calculate the probability p(r i,k ) of the ratio r i,k of the pixel point x i,j ;
5d)定义待处理像素点xi,j与其8邻域像素点xl之间的相似性距离dl为:5d) Define the similarity distance d l between the pixel x i,j to be processed and its 8 neighbor pixel x l as:
(6)对步骤(5)得到的相似性距离d1,d2,…,d8按升序排序,排序后的结果为取排序后距离为的像素点作为像素点xi,j的相似点,取像素点xi,j和这3个相似点灰度值的均值作为像素点xi,j预处理后的灰度值。(6) Sort the similarity distances d 1 , d 2 ,..., d 8 obtained in step (5) in ascending order, and the sorted result is Take the sorted distance as The pixel point of is used as the similar point of pixel point x i , j, and the average value of pixel point x i, j and the gray value of these three similar points is taken as the gray value of pixel point x i, j after preprocessing.
(7)重复步骤(2)~(6),计算出所有像素点的恢复值,得到预处理图像。(7) Repeat steps (2)~(6) to calculate the restored values of all pixels and obtain the preprocessed image.
本发明与现有的技术相比具有以下优点:Compared with the prior art, the present invention has the following advantages:
1.本发明采用方差系数将SAR图像分为两种区域:同质区域和纹理区域,对不同的区域采取不同的方法,从而提高了处理的精度。1. The present invention uses the variance coefficient to divide the SAR image into two types of regions: homogeneous region and texture region, and adopts different methods for different regions, thereby improving the processing accuracy.
2.本发明采用旋转操作,使块内同质区域处在相同的位置,因此计算的相似性更加准确,从而能够用相似度较高的像素点来修复当前点,更好的保持图像的纹理和细节信息;2. The present invention adopts the rotation operation, so that the homogeneous area in the block is at the same position, so the calculated similarity is more accurate, so that the current point can be repaired with a pixel with a higher similarity, and the texture of the image can be better maintained and details;
3.本发明采用基于比直分布概率相似性距离,能够更准确地计算SAR图像像素点之间的相似性;3. The present invention adopts the probability similarity distance based on the direct distribution, which can more accurately calculate the similarity between SAR image pixels;
附图说明Description of drawings
图1是本发明的流程图;Fig. 1 is a flow chart of the present invention;
图2是本发明使用的测试图像;Fig. 2 is the test image that the present invention uses;
图3是用现有的MMSE方法对图2进行预处理的结果图;Fig. 3 is the result figure that Fig. 2 is preprocessed with existing MMSE method;
图4是用现有的均值方法对图2进行预处理的结果图;Fig. 4 is the result figure that Fig. 2 is preprocessed with existing average method;
图5是用本发明方法对图2进行预处理的结果图Fig. 5 is the result figure that Fig. 2 is preprocessed with the method of the present invention
图6是用现有的MMSE方法对图2进行预处理的结果图的细节放大图。Fig. 6 is an enlarged view of the details of the preprocessed graph of Fig. 2 using the existing MMSE method.
图7是用现有的均值方法对图2进行预处理的结果图的细节放大图;Fig. 7 is the detailed enlargement figure of the result figure that Fig. 2 is preprocessed with existing average method;
图8是用本发明方法对图2进行预处理的结果图的细节放大图。Fig. 8 is a detailed enlarged view of the preprocessed result graph of Fig. 2 by the method of the present invention.
具体实施方式detailed description
参照附图1,本发明包括如下步骤:With reference to accompanying drawing 1, the present invention comprises the steps:
步骤1,对于输入大小为(m,n)的L视SAR图像v,计算出所有像素点的方差系数CV,得到方差系数矩阵K。Step 1. For an L-view SAR image v with an input size of (m, n), calculate the variance coefficient CV of all pixels, and obtain the variance coefficient matrix K.
1.1)计算像素点xi,j,i∈[1,m],j∈[1,n],的方差系数CVi,j:1.1) Calculate the variance coefficient CV i,j of pixel x i,j , i∈[1,m],j∈[1,n]:
其中,是以像素点xi,j为中心的7×7邻域内所有像素点灰度值的标准差,是以像素点xi,j为中心的7×7邻域内所有像素点灰度值的均值;in, is the standard deviation of the gray value of all pixels in the 7×7 neighborhood centered on pixel x i,j , is the mean value of the gray value of all pixels in the 7×7 neighborhood centered on pixel x i,j ;
1.2)计算出SAR图像v中每一像素点的方差系数,得到方差系数矩阵K0 1.2) Calculate the variance coefficient of each pixel in the SAR image v, and obtain the variance coefficient matrix K 0
K0={CVi,j},i∈[1,m],j∈[1,n]K 0 ={CV i,j },i∈[1,m],j∈[1,n]
1.3)对算出的方差系数矩阵K0进行3×3均值滤波,得到滤波后的方差系数矩阵K。1.3) Perform 3×3 mean value filtering on the calculated variance coefficient matrix K 0 to obtain the filtered variance coefficient matrix K.
步骤2,设定分类阈值Tcv,对输入的SAR图像v进行分类处理。Step 2, set the classification threshold T cv , and classify the input SAR image v.
2.1)根据输入的L视图像类别设定阈值Tcv:2.1) Set the threshold T cv according to the input L-view image category:
若输入的是L视幅度SAR图像,则阈值Tcv设定为:If the input is an L-magnitude SAR image, the threshold T cv is set as:
若输入的是L视强度SAR图像,则阈值Tcv设定为:If the input is an L apparent intensity SAR image, the threshold T cv is set as:
2.2)将像素点xi,j的方差系数CVi,j与阈值Tcv相比较,从而将图像分为两类处理,如果方差系数CVi,j小于阈值Tcv则执行步骤3,否则执行步骤4;2.2) Compare the variance coefficient CV i,j of the pixel point x i, j with the threshold T cv to divide the image into two types of processing. If the variance coefficient CV i,j is smaller than the threshold T cv , execute step 3, otherwise execute Step 4;
步骤3,将像素点xi,j为中心的3×3块内像素点灰度值的均值作为像素点xi,j预处理后的灰度值。Step 3, take the mean value of the gray value of the pixel points in the 3×3 block centered on the pixel point x i ,j as the preprocessed gray value of the pixel point x i,j.
步骤4,对像素点xi,j的8邻域像素点xl,l=1,2,…,8,取以像素点xl为中心的3×3块vl,对块vl采取旋转操作,使以像素点xi,j为中心的3×3块vi,j和以像素点xl为中心的3×3块vl中同质的区域处在相同的位置,把vl旋转后的块记为。Step 4, for pixel point x l in the 8 neighbors of pixel point x i,j , l=1,2,…,8, take a 3×3 block v l centered on pixel point x l , and take The rotation operation makes the homogeneous area in the 3×3 block v i,j centered on the pixel point x i ,j and the homogeneous area in the 3×3 block v l centered on the pixel point x l be in the same position, and v l The rotated block is denoted as .
步骤5,计算像素点xi,j与其8邻域像素点xl的相似性距离:Step 5, calculate the similarity distance between pixel x i, j and its 8 neighbor pixel x l :
5.1)取像素点xi,j为中心的3×3块vi,j,由步骤4得到其邻域像素点xl,l=1,2,…,8翻转后的的3×3块 5.1) Take the 3×3 block v i,j centered on the pixel point x i,j , and obtain the 3×3 block of its neighborhood pixel point x l by step 4, l=1,2,…,8 after flipping
5.2)计算上述两个像素块vi,j和的比值ri,k:5.2) Calculate the above two pixel blocks v i, j and The ratio r i,k of :
其中vi,k表示待处理像素点xi,j为中心的3×3块vi,j的第k个像素点的灰度值,vl,k表示像素点xl为中心的3×3块vl旋转后的块的第k个像素点的灰度值;Among them, v i,k represents the gray value of the kth pixel of the 3×3 block v i,j centered on the pixel point x i,j to be processed, and v l,k represents the 3× pixel point x l as the center 3 blocks v l rotated blocks The gray value of the kth pixel of ;
5.3)利用比值分布概率公式计算出像素点xi,j的比值ri,k出现的概率p(ri,k),分两种情况计算:5.3) Use the ratio distribution probability formula to calculate the probability p(ri ,k ) of the ratio r i,k of the pixel point x i,j , which is calculated in two cases:
如果输入的SAR图像v是幅度图像,则:If the input SAR image v is a magnitude image, then:
如果输入SAR图像v是强度图像,则:If the input SAR image v is an intensity image, then:
5.4)定义待处理像素点xi,j与其8邻域像素点xl之间的相似性距离dl为:5.4) Define the similarity distance d l between the pixel point x i,j to be processed and its 8 neighbor pixel points x l as:
分别计算出像素点xi,j与其8邻域像素点xl的相似性距离dl。Calculate the similarity distance d l between the pixel point x i,j and its 8 neighbor pixel point x l respectively.
步骤6,对步骤5得到的相似性距离d1,d2,…,d8按升序排序,排序后的结果为取排序后距离为的像素点作为像素点xi,j的相似点,取像素点xi,j和这3个相似点灰度值的均值作为像素点xi,j预处理后的灰度值。Step 6, sort the similarity distances d 1 , d 2 ,..., d 8 obtained in step 5 in ascending order, and the sorted result is Take the sorted distance as The pixel point of is used as the similar point of pixel point x i , j, and the average value of pixel point x i, j and the gray value of these three similar points is taken as the gray value of pixel point x i, j after preprocessing.
步骤7,重复步骤2~步骤6,计算出所有像素点的恢复值,得到预处理图像。Step 7, repeat steps 2 to 6, calculate the restored values of all pixels, and obtain the preprocessed image.
本发明效果可以通过以下实验进一步证实:Effect of the present invention can further confirm by following experiment:
一.实验条件和内容1. Experimental conditions and content
实验条件:实验所使用的输入图像是两视X波段幅度SAR图像(Bedfordshire),如图2所示。Experimental conditions: The input image used in the experiment is a two-view X-band amplitude SAR image (Bedfordshire), as shown in Figure 2.
实验方法:在上述实验条件下,选用当前图像预处理的两种典型的算法和本发明方法进行比较实验,它们分别是:(1)最小均方误差(MMSE)方法;(2)均值方法。Experimental method: under the above-mentioned experimental conditions, select two typical algorithms of current image preprocessing and the method of the present invention to carry out comparative experiment, they are respectively: (1) minimum mean square error (MMSE) method; (2) mean value method.
二.实验内容two. Experimental content
实验一:用现有MMSE方法对图2进行处理,其中块大小为3×3,实验结果如图3、图6所示,其中图6是图3的细节放大图。由图3和图6可以看出,MMSE方法的在同质区域对噪声有良好的抑制能力,但是边缘与细节变模糊,强反射点目标的亮度被压缩。Experiment 1: Process Figure 2 with the existing MMSE method, where the block size is 3×3, and the experimental results are shown in Figure 3 and Figure 6, where Figure 6 is an enlarged view of the details of Figure 3. It can be seen from Figure 3 and Figure 6 that the MMSE method has a good ability to suppress noise in homogeneous areas, but the edges and details become blurred, and the brightness of strong reflection points is compressed.
实验二:用现有均值方法对图2进行处理,其中块大小3×3,实验结果如图4、图7所示,其中图7是图4的细节放大图。由图7和图4结果图可以看出,均值方法噪声抑制能力稳定性要优于MMSE方法,但不能很好的保持图像的边缘和纹理信息,强反射点目标亮度被严重压缩。Experiment 2: Process Figure 2 with the existing mean value method, where the block size is 3×3, and the experimental results are shown in Figure 4 and Figure 7, where Figure 7 is a detailed enlarged view of Figure 4. It can be seen from the results in Figure 7 and Figure 4 that the stability of the noise suppression ability of the mean method is better than that of the MMSE method, but it cannot preserve the edge and texture information of the image very well, and the brightness of the strong reflection point target is severely compressed.
实验三:用本发明方法对图2进行处理,其中块大小为3×3,实验结果如图5、图8所示,其中图8是图5的细节放大图,由图8和图5的结果图可以看出,本发明能够有效的抑制图像噪声,且很好的保持图像的边缘和纹理信息,对强反射点目标有很好的保持性。Experiment three: process Fig. 2 with the inventive method, wherein block size is 3 * 3, experimental result is as shown in Fig. 5, Fig. 8, and wherein Fig. 8 is the detailed enlarged view of Fig. 5, by Fig. 8 and Fig. 5 It can be seen from the result figure that the present invention can effectively suppress image noise, and well preserve the edge and texture information of the image, and has good retention for strong reflection point targets.
以上实验结果表明,本发明在总体性能上优于其它两种同类的预处理方法,能够很好地平滑噪声的同时保持自然图像的边缘和纹理等细节,对强反射点目标有很好的保持性。The above experimental results show that the overall performance of the present invention is superior to other two similar preprocessing methods, and it can smooth the noise well while maintaining details such as edges and textures of natural images, and it has a good preservation of strong reflection point targets. sex.
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