CN104599233A - Method and device for quickly registering SAR and full-color image - Google Patents
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Abstract
本发明属于图像处理领域,具体而言,涉及一种SAR与全色图像快速配准方法及装置,其中,该方法包括步骤1,对参考图像及变换后的浮动图像进行数据分块,得到多个数据子块;步骤2,计算每个数据子块的联合概率分布矩阵及累积函数值;步骤3,合并每个数据子块的联合概率分布矩阵及累积函数值,迭代计算所述参考图像及浮动图像的CCRE。通过该方法本发明在保证配准精度的前提下,显著提高了配准速度。
The invention belongs to the field of image processing. Specifically, it relates to a method and device for rapid registration of SAR and panchromatic images. The method includes step 1: performing data block on a reference image and a transformed floating image to obtain multiple data sub-blocks; step 2, calculate the joint probability distribution matrix and cumulative function value of each data sub-block; step 3, merge the joint probability distribution matrix and cumulative function value of each data sub-block, iteratively calculate the reference image and CCRE for floating images. Through this method, the present invention significantly improves the registration speed while ensuring the registration accuracy.
Description
技术领域technical field
本发明属于图像处理领域,具体而言,涉及一种SAR与全色图像快速配准的方法及装置。The invention belongs to the field of image processing, and in particular relates to a method and device for rapid registration of SAR and panchromatic images.
背景技术Background technique
多源图像配准是指依据一些相似性度量决定图像间的变换参数,使从不同传感器、不同视角、不同时间获取的同一场景的两幅或多幅图像,变换到同一坐标系下,在像素层上得到最佳匹配的过程。Multi-source image registration refers to determining the transformation parameters between images based on some similarity measures, so that two or more images of the same scene acquired from different sensors, different perspectives, and different times are transformed into the same coordinate system, and the pixel The process of getting the best match on the layer.
遥感图像配准方法一般分为基于特征提取的图像配准方法和基于灰度信息的图像配准方法。基于特征提取的遥感图像配准方法中,最常使用的特征是Harris角点、Moravec角点、SIFT特征点等。上述方法存在以下不足:不同的场景需要选择不同的特征,没有一般的模型可循,有很强的应用局限性;容易受到噪声的影响。由于SAR本身的成像机理,精确提取特征十分困难,也很难设计出一种能同时针对不同噪声强度、不同分辨率以及含有不同场景结构的SAR图像都适用的特征提取算法,这将导致出现一种配准算法只适用于一类SAR图像的情况。因此,常用的基于特征的方法在SAR图像配准应用中具有很大的局限性。Remote sensing image registration methods are generally divided into image registration methods based on feature extraction and image registration methods based on gray information. In the remote sensing image registration method based on feature extraction, the most commonly used features are Harris corner points, Moravec corner points, SIFT feature points, etc. The above method has the following disadvantages: different scenes need to select different features, there is no general model to follow, and there are strong application limitations; it is easily affected by noise. Due to the imaging mechanism of SAR itself, it is very difficult to extract features accurately, and it is also difficult to design a feature extraction algorithm that can be applied to SAR images with different noise intensities, different resolutions, and different scene structures at the same time, which will lead to a series of problems. This registration algorithm is only suitable for one type of SAR image. Therefore, commonly used feature-based methods have great limitations in SAR image registration applications.
基于灰度信息的图像配准方法直接利用图像的灰度信息进行配准,通过对其像素间某种相似性度量的全局最优实现配准,灰度匹配中主要考虑其相似性度量方法。该方法不需对图像进行特别的预处理工作,不易受噪声影响,简单易行,较特征提取的方法其通用性更强。The image registration method based on gray information directly uses the gray information of the image for registration, and achieves registration through the global optimization of a certain similarity measure between pixels, and the similarity measure method is mainly considered in gray matching. This method does not need special preprocessing work on the image, is not easily affected by noise, is simple and easy to implement, and is more versatile than the method of feature extraction.
基于灰度统计特性的配准方法不需要进行图像分割和图像特征提取,因而可以避免由这些预处理所造成的精度损失。但是这种方法一般需要较大的计算量,配准的速度慢,对灰度信息变化非常敏感。每计算一次相似性测度,需要遍历图像中的所有像素,这显然使得计算量增大。在优化搜索的过程中,目前互信息函数局部极值问题主要通过引入更复杂的插值函数或者结合先验信息,获得更加光滑的函数曲线,会使整个计算过程有不同程度的增加。互信息(Mutual information,MI)和归一化互信息(Normalized mutualinformation,NMI)是广泛应用于配准领域的常用的函数,对于图像配准来说,采用互信息和归一化互信息往往很容易陷入局部极值,严重影响配准的精度。The registration method based on gray statistical properties does not need image segmentation and image feature extraction, so it can avoid the loss of precision caused by these preprocessing. However, this method generally requires a large amount of calculation, the speed of registration is slow, and it is very sensitive to changes in grayscale information. Every time the similarity measure is calculated, all pixels in the image need to be traversed, which obviously increases the amount of calculation. In the process of optimization search, the current mutual information function local extremum problem mainly obtains a smoother function curve by introducing more complex interpolation functions or combining prior information, which will increase the entire calculation process to varying degrees. Mutual information (Mutual information, MI) and normalized mutual information (Normalized mutual information, NMI) are commonly used functions widely used in the field of registration, for image registration, using mutual information and normalized mutual information is often very It is easy to fall into local extremum, which seriously affects the accuracy of registration.
发明内容Contents of the invention
本发明的目的在于提供一种SAR与全色图像快速配准的方法及装置,以解决上述的问题。The object of the present invention is to provide a method and device for rapid registration of SAR and panchromatic images, so as to solve the above problems.
在本发明的实施例中提供了一种SAR与全色图像快速配准的方法,包括:In an embodiment of the present invention, a method for rapid registration of SAR and panchromatic images is provided, including:
步骤1,对参考图像及变换后的浮动图像进行数据分块,得到多个数据子块;Step 1, performing data block on the reference image and the transformed floating image to obtain multiple data sub-blocks;
步骤2,计算每个数据子块的联合概率分布矩阵及累积函数值;Step 2, calculating the joint probability distribution matrix and cumulative function value of each data sub-block;
步骤3,合并每个数据子块的联合概率分布矩阵及累积函数值,迭代计算该参考图像及浮动图像的CCRE。Step 3, combining the joint probability distribution matrix and cumulative function value of each data sub-block, iteratively calculating the CCRE of the reference image and the floating image.
进一步,步骤1之前还包括:Further, before step 1, it also includes:
对该浮动图像进行图像分块,并对分块图像进行并行差值,得到上述变换后的浮动图像。The floating image is divided into image blocks, and parallel difference is performed on the block images to obtain the above-mentioned transformed floating image.
进一步,步骤3之后还包括:Further, after step 3, it also includes:
计算该CCRE与上次迭代得到的CCRE的差值,若该差值小于或者等于预设误差值,则结束迭代计算;若该差值大于预设误差值,则利用Powell算法调整变换参数后重复步骤1至步骤3。Calculate the difference between the CCRE and the CCRE obtained in the previous iteration. If the difference is less than or equal to the preset error value, the iterative calculation ends; if the difference is greater than the preset error value, use the Powell algorithm to adjust the transformation parameters and repeat. Step 1 to Step 3.
本发明的实施例还提供了一种SAR与全色图像快速配准的装置,包括:Embodiments of the present invention also provide a device for rapid registration of SAR and panchromatic images, including:
数据分块模块,用于对参考图像及变换后的浮动图像进行数据分块,得到多个数据子块;The data block module is used to carry out data block to the reference image and the transformed floating image to obtain a plurality of data sub-blocks;
计算模块,用于计算每个数据子块的联合概率分布矩阵及累积函数值;Calculation module, used to calculate the joint probability distribution matrix and cumulative function value of each data sub-block;
CCRE求取模块,用于合并每个数据子块的联合概率分布矩阵及累积函数值,迭代计算该参考图像及浮动图像的CCRE。The CCRE calculation module is used to combine the joint probability distribution matrix and the cumulative function value of each data sub-block, and iteratively calculate the CCRE of the reference image and the floating image.
进一步,该装置还包括:Further, the device also includes:
浮动图像变换模块,用于对该浮动图像进行图像分块,并对分块图像进行并行差值,得到上述变换后的浮动图像。The floating image transformation module is used for performing image block on the floating image, and performing parallel difference on the block image to obtain the above-mentioned transformed floating image.
进一步,该装置还包括:Further, the device also includes:
差值计算模块,用于计算该CCRE与上次迭代得到的CCRE的差值,若该差值小于或者等于预设误差值,则结束迭代计算;若该差值大于预设误差值,则利用Powell算法调整变换参数后重复该数据分块模块、计算模块及CCRE求取模块的动作步骤。The difference calculation module is used to calculate the difference between the CCRE and the CCRE obtained by the previous iteration, if the difference is less than or equal to the preset error value, then the iterative calculation is terminated; if the difference is greater than the preset error value, then use After the Powell algorithm adjusts the transformation parameters, the action steps of the data block module, the calculation module and the CCRE calculation module are repeated.
本发明实施例提供的一种SAR与全色图像快速配准的方法及装置与现有技术相比:Compared with the prior art, a method and device for rapid registration of SAR and panchromatic images provided by the embodiment of the present invention:
(1)针对传统互信息图像配准容易产生局部极值的问题,在SAR和全色图像配准算法中,本发明采用交叉累积剩余熵作为相似性测度,交叉累积剩余熵比香农熵更具一般性,与传统的采用互信息的配准方法相比,交叉累积剩余熵可以有效地避免局部极值,去除噪声,并在一定程度上加快了配准速度;(1) Aiming at the problem that traditional mutual information image registration is easy to generate local extremum, in SAR and panchromatic image registration algorithm, the present invention adopts cross-cumulative residual entropy as similarity measure, and cross-cumulative residual entropy is stronger than Shannon entropy In general, compared with the traditional registration method using mutual information, the cross-cumulative residual entropy can effectively avoid local extremum, remove noise, and speed up the registration to a certain extent;
(2)针对基于交叉累积剩余熵的SAR和全色图像配准算法存在的配准速度慢的问题,本发明提出一种并行优化策略,使用并行多线程的方法对基于交叉累积剩余熵的串行图像配准算法进行改进优化,在保证配准精度的前提下,显著提高了配准速度。(2) Aiming at the problem of slow registration speed in SAR and panchromatic image registration algorithms based on cross accumulated residual entropy, the present invention proposes a parallel optimization strategy. The image registration algorithm is improved and optimized, and the registration speed is significantly improved under the premise of ensuring the registration accuracy.
附图说明Description of drawings
图1示出了本发明一种SAR与全色图像快速配准的方法一种实施例的流程图;Fig. 1 shows a flow chart of an embodiment of a method for rapid registration of a SAR and a panchromatic image in the present invention;
图2示出了本发明一种SAR与全色图像快速配准的方法另一种实施例的流程图;Fig. 2 shows a flow chart of another embodiment of a method for fast registration of a SAR and a panchromatic image in the present invention;
图3示出了本发明一种SAR与全色图像快速配准的装置一种实施例的结构框图;Fig. 3 shows a structural block diagram of an embodiment of a device for rapid registration of a SAR and a panchromatic image in the present invention;
图4示出了本发明一种SAR与全色图像快速配准的装置另一种实施例的结构框图;Fig. 4 shows a structural block diagram of another embodiment of a device for rapid registration of a SAR and a panchromatic image in the present invention;
图5示出了本发明一种SAR与全色图像快速配准的方法的联合概率分布计算示意图;Fig. 5 shows a schematic diagram of joint probability distribution calculation of a method for rapid registration of SAR and panchromatic images according to the present invention;
图6示出了本发明一种SAR与全色图像快速配准的方法的数据分块并行计算示意图;Fig. 6 shows a schematic diagram of data block parallel computing of a method for rapid registration of SAR and panchromatic images according to the present invention;
图7示出了本发明一种SAR与全色图像快速配准的方法的插值测试结果分析图;FIG. 7 shows an analysis diagram of interpolation test results of a method for rapid registration of SAR and panchromatic images according to the present invention;
图8示出了本发明一种SAR与全色图像快速配准的方法的CCRE测试结果分析图;Fig. 8 shows a CCRE test result analysis diagram of a method for rapid registration of SAR and panchromatic images in the present invention;
图9示出了本发明一种SAR与全色图像快速配准的方法的配准结果分析图。FIG. 9 shows an analysis diagram of a registration result of a method for rapid registration of a SAR and a panchromatic image according to the present invention.
具体实施方式Detailed ways
下面通过具体的实施例子并结合附图对本发明做进一步的详细描述。The present invention will be described in further detail below through specific implementation examples and in conjunction with the accompanying drawings.
参图1所示,图1示出了本发明一种SAR与全色图像快速配准的方法一种实施例的流程图。Referring to FIG. 1 , FIG. 1 shows a flowchart of an embodiment of a method for rapid registration of a SAR and a panchromatic image according to the present invention.
本实施例提供了一种SAR与全色图像快速配准的方法,包括:This embodiment provides a method for rapid registration of SAR and panchromatic images, including:
步骤S102,对参考图像及变换后的浮动图像进行数据分块,得到多个数据子块;Step S102, performing data block on the reference image and the transformed floating image to obtain multiple data sub-blocks;
步骤S103,计算每个数据子块的联合概率分布矩阵及累积函数值;Step S103, calculating the joint probability distribution matrix and cumulative function value of each data sub-block;
步骤S104,合并每个数据子块的联合概率分布矩阵及累积函数值,迭代计算参考图像及浮动图像的CCRE。Step S104, merging the joint probability distribution matrix and cumulative function value of each data sub-block, iteratively calculating the CCRE of the reference image and the floating image.
本实施例采用交叉累积剩余熵作为相似性测度,交叉累积剩余熵比香农熵更具一般性,与传统的采用互信息的配准方法相比,交叉累积剩余熵可以有效地避免局部极值,去除噪声,并在一定程度上加快了配准速度;本实施例采用并行多线程的方法对基于交叉累积剩余熵的串行图像配准算法进行改进优化,在保证配准精度的前提下,显著提高了配准速度。This embodiment uses cross-cumulative residual entropy as a similarity measure, and cross-cumulative residual entropy is more general than Shannon entropy. Compared with the traditional registration method using mutual information, cross-cumulative residual entropy can effectively avoid local extremum, Noise is removed, and the registration speed is accelerated to a certain extent; this embodiment adopts the method of parallel multi-threading to improve and optimize the serial image registration algorithm based on cross-accumulated residual entropy. Improved registration speed.
参图2所示,图2示出了本发明一种SAR与全色图像快速配准的方法另一种实施例的流程图。Referring to FIG. 2 , FIG. 2 shows a flow chart of another embodiment of a method for rapid registration of a SAR and a panchromatic image according to the present invention.
在本实施例中,步骤S102之前还包括:In this embodiment, before step S102, it also includes:
步骤S101,对所述浮动图像进行图像分块,并对分块图像进行并行差值,得到上述变换后的浮动图像。In step S101 , image blocks are performed on the floating image, and parallel difference is performed on the block images to obtain the transformed floating image.
在本实施例中,步骤S104之后还包括:In this embodiment, after step S104, it also includes:
步骤S105,计算CCRE与上次迭代得到的CCRE的差值,若该差值小于或者等于预设误差值,则结束迭代计算;若该差值大于预设误差值,则利用Powell算法调整变换参数后重复上述步骤S102至步骤S104。Step S105, calculate the difference between the CCRE and the CCRE obtained in the last iteration, if the difference is less than or equal to the preset error value, then end the iterative calculation; if the difference is greater than the preset error value, then use the Powell algorithm to adjust the transformation parameters Then repeat the above step S102 to step S104.
参图3所示,图3示出了本发明一种SAR与全色图像快速配准的装置一种实施例的结构框图。Referring to FIG. 3 , FIG. 3 shows a structural block diagram of an embodiment of a device for rapid registration of a SAR and a panchromatic image according to the present invention.
本实施例还提供了一种SAR与全色图像快速配准的装置,包括:This embodiment also provides a device for rapid registration of SAR and panchromatic images, including:
数据分块模块22,用于对参考图像及变换后的浮动图像进行数据分块,得到多个数据子块;Data block module 22, for carrying out data block to reference image and transformed floating image, obtain a plurality of data sub-blocks;
计算模块23,用于计算每个数据子块的联合概率分布矩阵及累积函数值;Calculation module 23, used to calculate the joint probability distribution matrix and cumulative function value of each data sub-block;
CCRE求取模块24,用于合并每个数据子块的联合概率分布矩阵及累积函数值,迭代计算该参考图像及浮动图像的CCRE。The CCRE calculation module 24 is used to combine the joint probability distribution matrix and the cumulative function value of each data sub-block, and iteratively calculate the CCRE of the reference image and the floating image.
本实施例通过设置数据分块模块22、计算模块23及CCRE求取模块24,采用交叉累积剩余熵作为相似性测度,交叉累积剩余熵比香农熵更具一般性,与传统的采用互信息的配准方法相比,交叉累积剩余熵可以有效地避免局部极值,去除噪声,并在一定程度上加快了配准速度;本实施例采用并行多线程的方法对基于交叉累积剩余熵的串行图像配准算法进行改进优化,在保证配准精度的前提下,显著提高了配准速度。In this embodiment, by setting the data block module 22, the calculation module 23 and the CCRE calculation module 24, the cross-cumulative residual entropy is used as the similarity measure. The cross-cumulative residual entropy is more general than the Shannon entropy, and it is different from the traditional mutual information method. Compared with the registration method, the cross-accumulated residual entropy can effectively avoid local extremums, remove noise, and accelerate the registration speed to a certain extent; The image registration algorithm is improved and optimized, and the registration speed is significantly improved under the premise of ensuring the registration accuracy.
参图4所示,图4示出了本发明一种SAR与全色图像快速配准的装置另一种实施例的结构框图。Referring to FIG. 4 , FIG. 4 shows a structural block diagram of another embodiment of a device for rapid registration of a SAR and a panchromatic image according to the present invention.
在本实施例中,该装置还包括:In this embodiment, the device also includes:
浮动图像变换模块21,用于对该浮动图像进行图像分块,并对分块图像进行并行差值,得到上述变换后的浮动图像。The floating image conversion module 21 is configured to divide the floating image into image blocks, and perform parallel difference on the block images to obtain the transformed floating image.
在本实施例中,该装置还包括:In this embodiment, the device also includes:
差值计算模块25,用于计算该CCRE与上次迭代得到的CCRE的差值,若该差值小于或者等于预设误差值,则结束迭代计算;若该差值大于预设误差值,则利用Powell算法调整变换参数后重复数据分块模块22、计算模块23及CCRE求取模块24的动作步骤。The difference calculation module 25 is used to calculate the difference between the CCRE and the CCRE obtained by the last iteration, if the difference is less than or equal to the preset error value, then end the iterative calculation; if the difference is greater than the preset error value, then The action steps of the data block module 22 , the calculation module 23 and the CCRE calculation module 24 are repeated after the transformation parameters are adjusted by the Powell algorithm.
下面对本实施例提供的配准方法进行进一步详细描述。The registration method provided by this embodiment will be further described in detail below.
本实施例采用基于灰度信息的图像配准方法,利用CCRE(Cross cumulative residual entropy,交叉累积剩余熵)作为相似性测度。CCRE与传统的互信息相比,有许多优良的特性,使得配准可以得到更高精度。但基于灰度信息方法明显的缺陷是算法的时间复杂度高。针对基于CCRE的SAR和全色图像配准算法存在的配准速度慢的问题,本实施例采用并行优化策略,利用并行算法,首先对数据进行划分,然后把这些数据块分给不同的线程,使数据子块同时计算,以达到降低运行时间的目的。In this embodiment, an image registration method based on grayscale information is adopted, and CCRE (Cross cumulative residual entropy, cross cumulative residual entropy) is used as a similarity measure. Compared with the traditional mutual information, CCRE has many excellent characteristics, so that the registration can obtain higher accuracy. However, the obvious defect of the method based on gray information is the high time complexity of the algorithm. Aiming at the problem of slow registration speed in the SAR and panchromatic image registration algorithm based on CCRE, this embodiment adopts a parallel optimization strategy, utilizes a parallel algorithm, first divides the data, and then distributes these data blocks to different threads, The data sub-blocks are calculated at the same time to achieve the purpose of reducing the running time.
SAR和全色图像配准是相同场景下,使用不同的成像设备,在不同视角下获得的存在位移、旋转等的差异图像。在配准的过程中以一幅图像作为参考图像,一幅作为浮动图像,调整浮动图像,使两幅图像在位置上匹配。图像配准有数学上的定义,实现一个配准算法需要考虑特征空间、搜索空间、相似性测度、搜索策略等因素。SAR and panchromatic image registration are different images with displacement, rotation, etc. obtained under different viewing angles using different imaging devices in the same scene. In the process of registration, one image is used as a reference image, and the other is used as a floating image, and the floating image is adjusted to make the two images match in position. Image registration has a mathematical definition. To implement a registration algorithm, factors such as feature space, search space, similarity measure, and search strategy need to be considered.
图像配准模型:Image registration model:
设IF(x,y)、IR(x,y)分别表示浮动图像IF和参考图像IR在点(x,y)处的灰度值,图像IR、IF的配准关系可用公式(1)表示。Let I F (x, y) and I R (x, y) represent the gray value of the floating image I F and the reference image I R at point (x, y) respectively, and the registration relationship of the images I R and I F Available formula (1) said.
IR(x,y)=g(IF(f(x,y))) (1)I R (x,y)=g(I F (f(x,y))) (1)
其中f表示二维空间坐标几何变换,包括平移变换和旋转变换。g表示灰度变换函数。公式(1)的含义可以理解为,对图像IF进行变换,使图像IF、IR在对应的空间位置具有相同的灰度值。设T(·)表示对图像的变换,则图像配准可以用公式(2)描述。Where f represents the geometric transformation of two-dimensional space coordinates, including translation transformation and rotation transformation. g represents the grayscale transformation function. The meaning of the formula (1) can be understood as transforming the image I F so that the images I F and I R have the same gray value at corresponding spatial positions. Let T(·) denote the transformation of the image, then the image registration can be described by formula (2).
Find T0∈ΩFind T 0 ∈ Ω
Max S(T(IF),IR) (2)Max S(T(I F ),I R ) (2)
其中Ω是变换T(·)的可行域,S为相似性测度,Find…Max表示在域Ω中找出S的最大值。公式(2)表明图像配准即为在变换T(·)的可行空间内,寻找最优的变换T0,使变换后的浮动图像和参考图像具有最大的相似度。本实施例使用交叉累积剩余熵作为相似性测度。Where Ω is the feasible domain of transformation T(·), S is the similarity measure, and Find...Max means to find the maximum value of S in the domain Ω. Formula (2) shows that image registration is to find the optimal transformation T 0 in the feasible space of transformation T(·), so that the transformed floating image and the reference image have the greatest similarity. This embodiment uses cross-cumulative residual entropy as a similarity measure.
使用交叉累积剩余熵的匹配测度:Matching measure using cross-cumulated residual entropy:
在多源遥感图像中,SAR与光学图像成像原理不同,它们的特征在很大程度上形成互补。光学传感器只能在有光照的情况下采集图像,反映可见光波段的特性;SAR系统24小时连续工作,同时又反映了电磁波谱波段的特性,正好弥补光学系统的不足。但由于两种图像截然不同的成像机理,又使得两种图像信息集成面临很大挑战。In multi-source remote sensing images, SAR has different imaging principles from optical images, and their features complement each other to a large extent. The optical sensor can only collect images in the presence of light, reflecting the characteristics of the visible light band; the SAR system works continuously for 24 hours, and at the same time reflects the characteristics of the electromagnetic spectrum band, which just makes up for the shortcomings of the optical system. However, due to the completely different imaging mechanisms of the two images, the information integration of the two images faces great challenges.
SAR图像中相干噪声严重影响特征的提取,导致特征提取成功率降低。而光学图像中特征如何与SAR图像中相同的特征对应,直接影响配准成功率。The coherent noise in the SAR image seriously affects the feature extraction, resulting in a decrease in the success rate of feature extraction. How the features in the optical image correspond to the same features in the SAR image directly affects the registration success rate.
本实施例通过利用CCRE(Cross cumulative residual entropy,交叉累积剩余熵)来描述两组特征间的相似性,从而解决了SAR图像和光学图像中提取较精确且准确匹配的特征的困难。In this embodiment, CCRE (Cross cumulative residual entropy, cross cumulative residual entropy) is used to describe the similarity between two groups of features, thereby solving the difficulty of extracting more precise and accurately matched features in SAR images and optical images.
交叉累积剩余熵的主要特点为:适用于离散和连续两种环境;用分布范围代替分布数目;计算量小,对采样数据计算得到的结果更接近实际结果。应用于图像配准方面,相比互信息方法进行配准,具有两个明显优点:对噪声更为免疫、在参数变换域有更广泛的范围。The main characteristics of the cross-cumulative residual entropy are: it is suitable for both discrete and continuous environments; the distribution range is used instead of the distribution number; the calculation amount is small, and the results calculated for the sampled data are closer to the actual results. When applied to image registration, it has two obvious advantages compared with mutual information method registration: it is more immune to noise and has a wider range in the parameter transformation domain.
传统的互信息方法统计的是每个灰度值的直方图,反映的是一种离散特征,独立存在的噪声会对匹配结果产生不可忽视的影响。现有的离散的Shannon熵已经无法满足克服SAR图像上大量噪声的要求,需要一种能够反映一定区间内的累积变化的熵来减弱噪声对整体的影响。本实施例利用CCRE来反映一定区间内的累积变化,以达到减弱噪声对整体影响的目的,本实施例所述的CCRE是定义在累积剩余熵的基础上的,累积剩余熵的定义如公式(3)所示。The traditional mutual information method counts the histogram of each gray value, which reflects a discrete feature, and independent noise will have a non-negligible impact on the matching results. The existing discrete Shannon entropy can no longer meet the requirements of overcoming a large amount of noise on SAR images, and an entropy that can reflect the cumulative change in a certain interval is needed to weaken the influence of noise on the whole. This embodiment utilizes CCRE to reflect the cumulative change in a certain interval, so as to achieve the purpose of weakening the impact of noise on the whole. The CCRE described in this embodiment is defined on the basis of cumulative residual entropy, and the definition of cumulative residual entropy is as formula ( 3) as shown.
其中x是属于R的变量,F(λ)=P(|x|>λ),R+=(x∈R;x≥0)。与香农熵相比,公式(3)中用累积剩余函数F(λ)替换了概率密度函数p(x)。累积剩余函数比概率密度函数更具有一般性。因而由累积函数得到的CCRE比以概率密度函数得到的互信息具有更普遍的适用性。以h(x)表示香农熵,其定义如公式(4)所示。对于变量X、Y,它们的互信息MI定义为公式(5)。Where x is a variable belonging to R, F(λ)=P(|x|>λ), R+=(x∈R; x≥0). Compared with Shannon entropy, the probability density function p(x) is replaced by the cumulative residual function F(λ) in formula (3). Cumulative residual functions are more general than probability density functions. Therefore, the CCRE obtained by the cumulative function has more general applicability than the mutual information obtained by the probability density function. The Shannon entropy is represented by h(x), and its definition is shown in formula (4). For variables X and Y, their mutual information MI is defined as formula (5).
MI(X,Y)=h(X)+h(Y)-h(X,Y) (5)MI(X,Y)=h(X)+h(Y)-h(X,Y) (5)
其中h(X)、h(Y)分别为X、Y的熵,h(X,Y)为X、Y的联合熵。由公式(3),定义CCRE为公式(6)。Among them, h(X) and h(Y) are the entropy of X and Y respectively, and h(X,Y) is the joint entropy of X and Y. From formula (3), define CCRE as formula (6).
c(X,Y)=ε(X)-E[ε(X/Y)] (6)c(X,Y)=ε(X)-E[ε(X/Y)] (6)
对于离散的参考图像IR和变换后的浮动图像IT=T(IF),计算CCRE需要得到联合直方图,然后计算边缘概率密度和联合概率密度。离散的参考图像IR和变换后的浮动图像IT的CCRE可以用公式(7)表示。For the discrete reference image I R and the transformed floating image I T =T(I F ), the calculation of CCRE needs to obtain the joint histogram, and then calculate the edge probability density and the joint probability density. The CCRE of the discrete reference image I R and the transformed floating image IT can be expressed by Equation (7).
其中p(l,k)代表图像IR和IT的联合概率密度,pT(l)和pR(k)分别表示变换后的浮动图像和参考图像的边缘概率密度。LT和LR分别表示两幅图像离散像素点的集合。where p(l,k) represents the joint probability density of images IR and IT , and pT (l) and pR (k) represent the edge probability densities of the transformed floating image and the reference image, respectively. LT and LR represent the sets of discrete pixels of the two images respectively.
CCRE以累积分布函数替换互信息中的概率分布函数,使得CCRE对变换参数变化曲线更为平缓,改善互信息曲线中波动带来的局部极值问题。CCRE replaces the probability distribution function in the mutual information with the cumulative distribution function, which makes the CCRE change curve of the transformation parameter more gentle, and improves the local extremum problem caused by the fluctuation in the mutual information curve.
本实施例采用CCRE作为相似性测度,可以提高配准精度,不受SAR图像噪声的影响,比其他配准方法具有更快的收敛速度,计算量更低。In this embodiment, CCRE is used as the similarity measure, which can improve the registration accuracy, is not affected by SAR image noise, has faster convergence speed and lower calculation amount than other registration methods.
Powell搜索算法:Powell search algorithm:
图像配准中的几何变换有多种形式,如平移、旋转、缩放和扭曲等。选取不同的变换形式可以得到不同的变换空间。考虑由平移和旋转生成的搜索空间,它可由三个参数描述。水平位移x,竖直位移y和旋转角度θ,其中x,y均属于整数集N,-180°≤θ<180°。x,y,θ的所有组合构成了配准的搜索空间。如果对于每一个变换的组合(x,y,θ),都计算两幅图像的CCRE,这将是一个冗余又费时的过程。因而在配准过程中需要采用优化搜索算法,缩小搜索空间。Geometric transformations in image registration come in many forms, such as translation, rotation, scaling, and distortion. Different transformation spaces can be obtained by selecting different transformation forms. Consider the search space generated by translation and rotation, which can be described by three parameters. Horizontal displacement x, vertical displacement y and rotation angle θ, where x and y both belong to the integer set N, -180°≤θ<180°. All combinations of x, y, θ constitute the search space for registration. If for every combination of transformations (x, y, θ), the CCRE of two images is calculated, it will be a redundant and time-consuming process. Therefore, an optimized search algorithm needs to be used in the registration process to reduce the search space.
CCRE不易计算对变换参数(x,y,θ)的导数,Powell算法相对其他的优化算法,如牛顿法、梯度下降法,不需计算导数,又可以将CCRE的极值问题简化为一维极值问题,收敛速度快,有明显的加速配准效果。该算法具体步骤如下:CCRE is not easy to calculate the derivative of the transformation parameters (x, y, θ). Compared with other optimization algorithms, such as Newton method and gradient descent method, the Powell algorithm does not need to calculate the derivative, and can simplify the extreme value problem of CCRE to a one-dimensional extreme value problem, the convergence speed is fast, and there is an obvious effect of accelerating registration. The specific steps of the algorithm are as follows:
①给定算法初始点x0和n个线性无关的方向:d(1,1),d(1,2),…,d(1,n),允许的误差为ε>0,令k=1;① Given the initial point x 0 of the algorithm and n linearly independent directions: d (1,1) , d (1,2) , ..., d (1, n) , the allowable error is ε>0, let k= 1;
②令x(k,0)=xk-1,从x(k,0)出发,依次沿方向d(k,1),d(k,2),…,d(k,n)进行搜索,即令②Let x (k,0) =x k-1 , starting from x (k,0) , search along the directions d (k,1) , d (k,2) ,...,d (k,n) in sequence , even if
在每个方向上得到一点,x(k,1),x(k,2),…,x(k,n),令d(k,n+1)=x(k,n)–x(k,0),从x(k,n)出发沿着方向d(k,n+1)进行一维搜索得到xk;Get a point in each direction, x (k,1) , x (k,2) , ..., x (k,n) , let d (k,n+1) = x (k,n) –x ( k,0) , start from x (k,n) and perform a one-dimensional search along the direction d (k,n+1) to get x k ;
③如果||xk-xk-1||<ε,则停止搜索,点xk即为最优解,否则,令③If ||x k -x k-1 ||<ε, then stop searching, point x k is the optimal solution, otherwise, set
d(k+1,j)=x(k,j+1)(j=1,2,…,n),k=k+1,返回步骤②。d (k+1,j) =x (k,j+1) (j=1,2,...,n), k=k+1, return to step ②.
Powell算法虽然具有较快的收敛速度,大大缩小了搜索空间,但是在实际配准操作中,尤其对于大幅图像的配准,仍然是一项十分耗时的工作。为了进一步缩短配准时间,本发明采用并行策略加速配准算法。Although the Powell algorithm has a fast convergence speed and greatly reduces the search space, it is still a very time-consuming task in the actual registration operation, especially for large-scale image registration. In order to further shorten the registration time, the present invention adopts a parallel strategy to accelerate the registration algorithm.
并行策略:Parallel strategy:
并行处理计算的实现离不开并行计算机体系结构。按照Flynn分类法,将并行计算机分为单指令流单数据流计算机(SingleInstruction Single Data,SISD)、单指令流多数据流计算机(SingleInstruction Multiple Data,SIMD)、多指令流单数据流计算机(Multiple Instruction Single Data,MISD)和多指令流多数据流计算机(Multiple Instruction Multiple Data,MIMD)。其中MIMD系统可以并行执行多个子任务来降低整个程序的执行时间,是最广泛应用的并行计算机结构。The realization of parallel computing is inseparable from the parallel computer architecture. According to Flynn's taxonomy, parallel computers are divided into single instruction stream single data stream computer (Single Instruction Single Data, SISD), single instruction stream multiple data stream computer (Single Instruction Multiple Data, SIMD), multiple instruction stream single data stream computer (Multiple Instruction Single Data, MISD) and multiple instruction stream multiple data stream computer (Multiple Instruction Multiple Data, MIMD). Among them, the MIMD system can execute multiple subtasks in parallel to reduce the execution time of the entire program, and is the most widely used parallel computer structure.
针对MIMD系统的结构特点,设计并行算法时把一个较大的计算问题分解为若干个相对独立的子问题。假设可以同时使用的处理机的个数为m,对于定义在可行域Ω上的问题P,将可行域分解成Ω1,Ω2,…,Ωm,对应的子问题为P1,P2,…,Pm。为每一个处理机分配一个子问题,使子问题可以同时得到解决。所有子问题解决后,合并子问题的解得到问题P的解。According to the structural characteristics of the MIMD system, when designing a parallel algorithm, a large calculation problem is decomposed into several relatively independent sub-problems. Assuming that the number of processors that can be used at the same time is m, for the problem P defined on the feasible domain Ω, the feasible domain is decomposed into Ω 1 , Ω 2 ,..., Ω m , and the corresponding sub-problems are P 1 , P 2 ,..., P m . Assign a subproblem to each processor so that the subproblems can be solved simultaneously. After all the sub-problems are solved, the solutions of the sub-problems are combined to obtain the solution of problem P.
为了使子问题可以同时求解,子问题之间不应有很强的依赖关系。如果子问题Ps依赖其它某个子问题Pt的解,则Ps需要等待Pt求解完成后才能开始求解。当很多子问题满足以上情况时,上述方案只会带来任务分解产生的时间开销,不会减少问题的求解时间。In order for the subproblems to be solved simultaneously, there should not be strong dependencies between the subproblems. If the sub-problem P s depends on the solution of some other sub-problem P t , then P s needs to wait for the solution of P t to be completed before starting to solve it. When many sub-problems meet the above conditions, the above scheme will only bring time overhead caused by task decomposition, and will not reduce the solution time of the problem.
本实施例的配准算法中,需要多次计算CCRE。CCRE的计算依赖Powell算法每轮迭代后得到的新的变换参数,将计算CCRE作为并行算法的子问题将达不到优化提速的效果。本实施例的配准算法在利用Powell寻找最优解的过程中,每一轮迭代计算CCRE时,都需要计算参考图像和浮动图像的联合概率分布及累积函数,进而求解CCRE,因而可以将计算CCRE进行分解。In the registration algorithm of this embodiment, the CCRE needs to be calculated multiple times. The calculation of CCRE relies on the new transformation parameters obtained after each iteration of the Powell algorithm, and calculating CCRE as a sub-problem of the parallel algorithm will not achieve the effect of optimization and speed-up. In the registration algorithm of this embodiment, in the process of using Powell to find the optimal solution, each round of iterative calculation of CCRE needs to calculate the joint probability distribution and cumulative function of the reference image and the floating image, and then solve the CCRE, so the calculation can be CCRE does the decomposition.
以计算联合概率分布为例,可设参考图像IR的灰度级数为M,经过变换后的浮动图像IT的灰度级为N,则它们的联合概率分布如公式(8)所示。Taking the calculation of the joint probability distribution as an example, the gray level of the reference image I R can be set as M, and the gray level of the transformed floating image IT is N, then their joint probability distribution is shown in formula (8) .
联合概率分布是通过遍历两幅图像,根据对应位置像素的灰度值得到的,计算过程如图5所示。联合概率分布中每个元素的计算只由共享的图像IR和IT得到,不依赖其它元素的计算,这为数据的划分提供了便利。The joint probability distribution is obtained by traversing the two images and according to the gray value of the pixel at the corresponding position. The calculation process is shown in Figure 5. The calculation of each element in the joint probability distribution is only obtained from the shared images I R and IT , without relying on the calculation of other elements, which facilitates the division of data.
数据可按行或按列划分,最终的目的是使每个数据子块的计算量相当,这样可以充分利用每个线程的计算能力,达到负载均衡。联合概率密度的每个元素有相似的计算形式,消耗的计算能力相当,因而两种划分方式均可实现计算任务的均匀分配。根据运行环境可提供的线程数,可将计算任务平均地分配给每个线程。图6给出了有p个线程的运行环境下,按行分块的示意图。Data can be divided by row or column, and the ultimate goal is to make the amount of calculation of each data sub-block equal, so that the computing power of each thread can be fully utilized to achieve load balancing. Each element of the joint probability density has a similar calculation form and consumes the same computing power, so the two division methods can achieve a uniform distribution of computing tasks. According to the number of threads available in the operating environment, computing tasks can be evenly distributed to each thread. Fig. 6 shows a schematic diagram of partitioning by row in an operating environment with p threads.
并行运行阶段,等待每个线程计算结束后,将所有线程计算结果汇总,得到最终的计算结果。理论上,按这种策略实现的并行算法可将计算时间减少到之前的1/p。但每个线程得到的计算数据存在差异,导致计算任务不可能同步完成,加之线程之间通信存在的时间开销,往往使得实际运行时间大于理论用时。在配准算法中的图像插值、图像复制等操作里,数据均有上述弱相关性的特点,可实施该并行策略。按照上述思路将配准算法进行并行优化,可实现快速配准。In the parallel running stage, after waiting for the calculation of each thread to finish, the calculation results of all threads are aggregated to obtain the final calculation result. Theoretically, a parallel algorithm implemented by this strategy can reduce the calculation time to 1/p before. However, there are differences in the calculation data obtained by each thread, which makes it impossible to complete the calculation tasks synchronously. In addition, the time overhead of communication between threads often makes the actual running time longer than the theoretical time. In the operations of image interpolation and image copying in the registration algorithm, the data all have the characteristics of the above-mentioned weak correlation, so this parallel strategy can be implemented. According to the above ideas, the registration algorithm is optimized in parallel to achieve fast registration.
并行配准算法:Parallel registration algorithm:
①为变换图像准备存储空间并进行分块,按照变换参数求得逆变换矩阵,计算变换图像各像素点在浮动SAR图像上的对应位置,由浮动图像并行插值得到变换图像;① Prepare the storage space for the transformed image and divide it into blocks, obtain the inverse transformation matrix according to the transformation parameters, calculate the corresponding position of each pixel of the transformed image on the floating SAR image, and obtain the transformed image by parallel interpolation of the floating image;
②运用并行策略,首先对联合概率分布矩阵分块,由参考图像和变换后的浮动图像,并行计算得到联合概率分布矩阵,并得到累积函数值,合并数据块,计算参考全色图像和浮动图像的CCRE;②Using the parallel strategy, first divide the joint probability distribution matrix into blocks, calculate the joint probability distribution matrix from the reference image and the transformed floating image in parallel, and obtain the cumulative function value, merge the data blocks, and calculate the reference panchromatic image and floating image CCRE;
③如果CCRE与上次迭代得到的CCRE之差大于设定的允许误差,使用Powell算法调整变化参数,跳转到步骤①,否则表明已经满足配准要求,得到配准的浮动图像,配准过程结束。③If the difference between the CCRE and the CCRE obtained in the last iteration is greater than the set allowable error, use the Powell algorithm to adjust the change parameters, and jump to step ①, otherwise it means that the registration requirements have been met, and the registered floating image is obtained. The registration process Finish.
由上述①至③可知,图像数据参与的数据操作中均运用并行策略进行优化加速,而参数的判断和调整均没有采用并行策略。考虑到参数的调整和判断具有较小的运行开销,因而该配准算法可以实现较好的加速效果。From the above ① to ③, it can be seen that the parallel strategy is used for optimization and acceleration in the data operation involving image data, but the parallel strategy is not used in the judgment and adjustment of parameters. Considering that the adjustment and judgment of parameters have a small running cost, the registration algorithm can achieve a better acceleration effect.
并行配准算法测试与分析:Parallel registration algorithm testing and analysis:
硬件测试环境配置为:16枚Xeon E5649 CPU,主频2.40GHz,内存总容量16G,磁盘总容量3T。软件环境为:Microsoft VisualStudio 2005,OpenMP。测试使用多组同一区域的SAR和全色图像,本实施例列出了四组测试数据实验结果。The hardware test environment configuration is: 16 Xeon E5649 CPUs, the main frequency is 2.40GHz, the total memory capacity is 16G, and the total disk capacity is 3T. The software environment is: Microsoft VisualStudio 2005, OpenMP. The test uses multiple sets of SAR and panchromatic images of the same area, and this embodiment lists the experimental results of four sets of test data.
首先用不同尺寸的SAR浮动图像对串行和并行插值算法进行了测试。变换参数(x,y,θ)取(128,64,-64°)。表1给出了尺寸为1024×906的插值测试的结果。The serial and parallel interpolation algorithms are first tested with SAR floating images of different sizes. Transformation parameters (x, y, θ) take (128, 64, -64°). Table 1 presents the results of interpolation tests with size 1024×906.
表1 插值测试结果Table 1 Interpolation test results
表1中加速比定义为串行运行时间与并行运行时间的比值,直观地表示了并行加速效果。并行效率为加速比与线程数的比值,表示每个线程在算法中的平均使用率。它的数值越大,表示算法运行过程中具有更长的并行时间。其它尺寸图像测试结果如图7所示。The speedup ratio in Table 1 is defined as the ratio of the serial running time to the parallel running time, which intuitively shows the effect of parallel speedup. Parallel efficiency is the ratio of the speedup ratio to the number of threads, indicating the average utilization rate of each thread in the algorithm. The larger its value, the longer the parallelism time during the running of the algorithm. The test results of images of other sizes are shown in Figure 7.
由图7可知,随着线程数的增加,并行算法的加速效果不断变好。由尺寸较小的图像测试结果可知,当线程数达到一定数量,加速比不会继续增加。而过多的线程数会增加进程通信的负担,反而会导致加速比下降。It can be seen from Figure 7 that as the number of threads increases, the acceleration effect of the parallel algorithm continues to improve. It can be seen from the test results of images with a smaller size that when the number of threads reaches a certain number, the speedup ratio will not continue to increase. Too many threads will increase the burden of process communication, but will lead to a decrease in speed-up ratio.
图8为计算单次CCRE的加速比测试结果。由图8可知,运用并行策略计算CCRE亦可提高计算速度。Figure 8 shows the speedup test results for calculating a single CCRE. It can be seen from Fig. 8 that using the parallel strategy to calculate CCRE can also improve the calculation speed.
最后对整体配准算法测试。一幅SAR图像和相同尺寸的全色图像作为一组测试数据。分别测试基于互信息和CCRE串行配准算法,以及不同线程数的基于CCRE并行算法。Finally, the overall registration algorithm is tested. A SAR image and a panchromatic image of the same size are used as a set of test data. Test the serial registration algorithm based on mutual information and CCRE, and the parallel algorithm based on CCRE with different numbers of threads.
表2为基于互信息和CCRE的测试结果。表中配准精度由配准结果图像偏离参考图像的最大像素数来衡量。Table 2 shows the test results based on mutual information and CCRE. The registration accuracy in the table is measured by the maximum number of pixels that the registration result image deviates from the reference image.
表2 基于互信息和CCRE串行配准结果Table 2 Serial registration results based on mutual information and CCRE
由表2可知,本实施例实现的基于CCRE的图像配准算法配准速度更快,相比基于互信息的配准方法配准精度更高。It can be seen from Table 2 that the CCRE-based image registration algorithm implemented in this embodiment has faster registration speed and higher registration accuracy than the mutual information-based registration method.
本实施例使用多组图像对并行配准算法进行测试。尺寸为1024×906的一组图像测试结果如表3所示。其它组配准测试结果如图9所示。In this embodiment, multiple groups of images are used to test the parallel registration algorithm. A set of image test results with a size of 1024×906 are shown in Table 3. The registration test results of other groups are shown in Fig. 9 .
表3 并行配准测试结果Table 3 Parallel registration test results
综合表3和图9测试结果可以看到,并行加速比随进程数的增加而变大,并且所有加速比的值大于1。这表明并行算法起到了加速配准的作用,并且并行线程数的增加会使加速效果变得越来越明显。若配准图像尺寸较小,应当避免并行线程数目过多。Based on the test results in Table 3 and Figure 9, it can be seen that the parallel speedup ratio increases with the increase of the number of processes, and all speedup ratios are greater than 1. This shows that the parallel algorithm plays a role in accelerating the registration, and the increase in the number of parallel threads will make the acceleration effect more and more obvious. If the size of the registered image is small, an excessive number of parallel threads should be avoided.
加速比的增长速率随着线程数目增加而放缓,这是由于配准算法中存在串行执行部分。线程的增多只会减少并行部分的执行时间,串行部分占总执行时间的比例会不断上升,这就导致了加速比增长速率的放缓。The growth rate of the speedup slows down as the number of threads increases, which is due to the serial execution part of the registration algorithm. The increase in threads will only reduce the execution time of the parallel part, and the proportion of the serial part to the total execution time will continue to rise, which leads to a slowdown in the growth rate of the speedup ratio.
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.
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