CN105005993B - A kind of quick fine matching method of dimensional topography based on isomery projection - Google Patents
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Abstract
本发明公开了一种基于异构投影的三维地形快速精确匹配方法。该三维地形匹配方法包括三维DEM(Digital Elevation Model)地形数据的获取与转换,对三维地形进行正射投影,根据地形的正射投影再进行投影图匹配。对投影图进行匹配过程中采用直线特征与点特征结合的方法,在对投影图进行直线检测并匹配完同名直线之后,找到同名直线两两相交的虚拟角点,并计算出该角点的坐标。然后利用改进的SURF(Speed‑up robust features)算法对找出的虚拟角点进行匹配,改进的SURF算法结合了SURF算法、HARRIS算法和NCC(Normalized Cross Correlation)算法。当且仅当对应的角点匹配时,才认为这两条直线正确匹配。计算出投影图之间的变换关系,再将变换参数应用于三维地形之间的匹配,从而完成整个地形匹配过程。
The invention discloses a fast and accurate three-dimensional terrain matching method based on heterogeneous projection. The three-dimensional terrain matching method includes acquisition and conversion of three-dimensional DEM (Digital Elevation Model) terrain data, performing orthographic projection on the three-dimensional terrain, and performing projection map matching according to the orthographic projection of the terrain. In the process of matching the projection image, the method of combining the straight line feature and the point feature is adopted. After the line detection is performed on the projection image and the straight line with the same name is matched, the virtual corner point where the two straight lines with the same name intersect is found, and the coordinates of the corner point are calculated. . Then use the improved SURF (Speed-up robust features) algorithm to match the found virtual corners. The improved SURF algorithm combines SURF algorithm, HARRIS algorithm and NCC (Normalized Cross Correlation) algorithm. Two lines are considered to match correctly if and only if the corresponding corner points match. Calculate the transformation relationship between the projection maps, and then apply the transformation parameters to the matching between the three-dimensional terrain, so as to complete the whole terrain matching process.
Description
技术领域technical field
本发明属于三维地形匹配领域,具体为一种基于异构投影的三维地形快速精确匹配方法。The invention belongs to the field of three-dimensional terrain matching, in particular to a fast and accurate three-dimensional terrain matching method based on heterogeneous projections.
背景技术Background technique
地形匹配技术是地形辅助导航的关键技术之一,它在航空领域应用广泛,在水下运载体的海底地形匹配定位、机器人导航定位以及陆地车辆导航等方面,也有着广阔的应用前景。Terrain matching technology is one of the key technologies for terrain-assisted navigation. It is widely used in the aviation field, and it also has broad application prospects in underwater vehicle terrain matching and positioning, robot navigation and positioning, and land vehicle navigation.
现有的地形匹配算法很多,国内外也一直有人在研究,算法也在不断的更新改进。现有的方法中,有在高斯曲率图像中利用高斯曲率最大和最小的点作为特征点进行地形匹配的方法。有用归一化的小波描述符进行轮廓的描述和匹配的基于地形轮廓的匹配方法,但是此方法在地形轮廓不明显等情况不适用。有利用视觉词典的方法证明直接的2D到3D的匹配方法能提高匹配性能,但是此方法数据量太大的话就会影响内存,耗费的时间长,匹配过程中没有进行误描述符的剔除。后来出现了一种基于面特征的三维地形匹配算法,此方法的缺点是如果地形数据太大或者地形图复杂,匹配的时间会很长,匹配速度太慢。为此,本发明提出了一种基于地形正射投影的新的三维地形快速精确匹配方法,充分利用地形的表面特征,很多情形下投影图之间不是同源的,而是异构的图像。异构的图像缺乏灰度信息,传统的图像匹配方法已经不适用。本发明利用直线特征与点特征结合的方法进行投影图匹配。首先利用直线匹配算法匹配图像中的同名直线,对同名直线两两相交的交点作为虚拟角点,再利用点特征进行第二次匹配,剔除误匹配的直线。对点特征进行匹配时采用的是具有更高匹配精度的改进SURF(Speed-up robust features)算法,该算法结合了SURF算法、HARRIS算法和NCC(Normalized Cross Correlation)算法。There are many existing terrain matching algorithms, and people at home and abroad have been studying them, and the algorithms are constantly being updated and improved. Among the existing methods, there is a method of using the points with the largest and smallest Gaussian curvatures in the Gaussian curvature image as feature points for terrain matching. A matching method based on terrain contours is used to describe and match contours using normalized wavelet descriptors, but this method is not suitable for situations where the contours of the terrain are not obvious. There is a method using visual dictionaries to prove that the direct 2D to 3D matching method can improve the matching performance, but if the amount of data in this method is too large, it will affect the memory and take a long time, and the wrong descriptors are not eliminated during the matching process. Later, a 3D terrain matching algorithm based on surface features appeared. The disadvantage of this method is that if the terrain data is too large or the terrain map is complex, the matching time will be very long and the matching speed will be too slow. For this reason, the present invention proposes a new fast and accurate matching method for 3D terrain based on terrain orthographic projection, which makes full use of the surface features of the terrain. In many cases, the projection images are not homogeneous but heterogeneous images. Heterogeneous images lack grayscale information, and traditional image matching methods are no longer applicable. The present invention uses the method of combining the straight line feature and the point feature to match the projection image. Firstly, the straight line matching algorithm is used to match the straight lines with the same name in the image, and the intersection point of two straight lines with the same name is used as a virtual corner point, and then the point feature is used for the second matching, and the wrongly matched straight lines are eliminated. When matching point features, the improved SURF (Speed-up robust features) algorithm with higher matching accuracy is used, which combines SURF algorithm, HARRIS algorithm and NCC (Normalized Cross Correlation) algorithm.
发明内容Contents of the invention
本发明的目的是提供一种基于异构投影的三维地形快速精确匹配方法,以便提高三维地形匹配效率和匹配精度。The purpose of the present invention is to provide a fast and accurate matching method for three-dimensional terrain based on heterogeneous projections, so as to improve the matching efficiency and accuracy of three-dimensional terrain.
本发明的目的是这样实现的,一种基于异构投影的三维地形快速精确匹配方法,其特征在于,至少包括如下步骤:The purpose of the present invention is achieved in this way, a method for fast and accurate matching of three-dimensional terrain based on heterogeneous projections, characterized in that it at least includes the following steps:
步骤1,将原始数字高程模型地形数据格式USGS-DEM转换为数字高程模型地形数据格式CNSTDF-DEM;Step 1, convert the original digital elevation model terrain data format USGS-DEM into digital elevation model terrain data format CNSTDF-DEM;
步骤2,对步骤1所述地形格式的参考三维地形和待匹配三维地形进行正射投影;Step 2, performing orthographic projection on the reference 3D terrain of the terrain format described in step 1 and the 3D terrain to be matched;
步骤3,根据步骤2所述的正射投影,对投影图进行匹配,得到投影图变换参数,匹配过程采用直线特征和点特征结合的方法;Step 3, according to the orthographic projection described in step 2, the projection map is matched to obtain the projection map transformation parameters, and the matching process adopts the method of combining line features and point features;
步骤4,根据步骤3所述的变换参数,对参考三维地形和待匹配三维地形进行匹配并得到匹配结果。Step 4, according to the transformation parameters described in step 3, match the reference 3D terrain and the 3D terrain to be matched and obtain a matching result.
所述步骤1,包括如下步骤:Said step 1 includes the following steps:
步骤11,打开一个地形文件;Step 11, open a terrain file;
步骤12,判断打开的文件是否为USGS-DEM地形数据格式;Step 12, judging whether the opened file is in the USGS-DEM terrain data format;
步骤13,如果不是则转到步骤11,如果是则开始读取数据头;Step 13, if not then go to step 11, if it is then start to read the data header;
步骤14,提取相关地形文件头信息;Step 14, extracting relevant terrain file header information;
步骤15,存储文件头信息;Step 15, storing file header information;
步骤16,开辟数据存储空间;Step 16, open up data storage space;
步骤17,读取数据体;Step 17, read the data body;
步骤18,过滤数据体的每行的前144个字节;Step 18, filtering the first 144 bytes of each line of the data body;
步骤19,存储数据相关信息;Step 19, storing data-related information;
步骤110,结合存储的文件头和数据体保存为CNSTDF-DEM地形格式文件;Step 110, save as CNSTDF-DEM terrain format file in conjunction with stored file header and data body;
步骤111,地形转换完成。Step 111, terrain conversion is completed.
所述步骤2,包括如下步骤:Said step 2 comprises the following steps:
步骤21,打开得到的三维CNSTDF-DEM地形数据;Step 21, open the obtained three-dimensional CNSTDF-DEM terrain data;
步骤22,得到地形表面纹理特征;Step 22, obtaining terrain surface texture features;
步骤23,得到三维地形纹理;Step 23, obtaining a three-dimensional terrain texture;
步骤24,根据纹理得到地形正射投影图。In step 24, the terrain orthographic projection map is obtained according to the texture.
所述步骤3,包括如下步骤:Said step 3 includes the following steps:
步骤31,对待匹配投影图和参考投影图进行直线的检测与匹配,匹配完同名直线后,在待匹配投影图中将两两直线相交的交点作为待匹配投影图虚拟角点;Step 31, performing line detection and matching on the projection image to be matched and the reference projection image, after matching the straight lines with the same name, using the intersection point where two straight lines intersect in the projection image to be matched as the virtual corner point of the projection image to be matched;
步骤32,在参考投影图中将两两直线相交的交点作为参考投影图虚拟角点;Step 32, using the intersection point where two straight lines intersect in the reference projection diagram as the virtual corner point of the reference projection diagram;
步骤33,对虚拟角点进行SURF算法粗匹配;Step 33, performing rough matching of SURF algorithm on virtual corner points;
步骤34,对虚拟角点进行SURF算法精匹配;Step 34, performing SURF algorithm fine matching on the virtual corner points;
步骤35,通过第一次匹配,得到了参考图像和待配准图像的精确特征点对集合φAB,通过φAB便可以求取参考图像和待配准图像间的透视变换矩阵H;Step 35, through the first matching, the accurate feature point pair set φ AB of the reference image and the image to be registered is obtained, and the perspective transformation matrix H between the reference image and the image to be registered can be obtained through φ AB ;
步骤36,对待匹配投影图根据求出的变换矩阵H进行变换;Step 36, transforming the projection image to be matched according to the obtained transformation matrix H;
步骤37,对待匹配投影图进行插值;Step 37, interpolating the projection image to be matched;
步骤38,变换插值之后得到中间图像;Step 38, obtain the intermediate image after transformation and interpolation;
步骤39,获得参考投影图和待匹配投影图;Step 39, obtaining the reference projection image and the projection image to be matched;
步骤310,找出参考投影图和待匹配投影图之间的重叠部分作为各自的感兴趣区域,并且根据重叠区域大小划分参考图像为多块子区域。当重叠区域较大时,子区域大小为64×64,当重叠区域较小时子区域也相应的变小;Step 310, find out the overlapping parts between the reference projection image and the projection image to be matched as respective regions of interest, and divide the reference image into multiple sub-regions according to the size of the overlapping regions. When the overlapping area is larger, the size of the sub-area is 64×64, and when the overlapping area is smaller, the sub-area becomes smaller accordingly;
步骤311,在参考投影图的子区域中,以区域中心为中心,在周围32×32,即参考图像子区域大小的0.5倍邻域内进行HARRIS特征点提取,取该区域中所有HARRIS特征点中R值最大的,即与周围点最有区分度的点作为参考投影图的特征点;如果在32×32邻域内没有HARRIS特征点,则把子区域中心作为一个特征点处理;Step 311, in the sub-region of the reference projection image, take the center of the region as the center, and perform HARRIS feature point extraction in a neighborhood of 32×32, that is, 0.5 times the size of the reference image sub-region, and take all the HARRIS feature points in this region The point with the largest R value, that is, the most distinguishable point from the surrounding points is used as the feature point of the reference projection map; if there is no HARRIS feature point in the 32×32 neighborhood, the center of the sub-region is treated as a feature point;
步骤312,当所有的参考投影图特征点提取完后,进行NCC算法匹配;Step 312, after all the reference projection map feature points are extracted, perform NCC algorithm matching;
步骤313,在中间图像中,以参考投影图特征点坐标为中心的96×96,即参考投影图子区域大小的1.5倍邻域内进行搜索,记录相关系数和其特征点坐标,得到粗匹配点;Step 313, in the intermediate image, search within the neighborhood of 96×96 centered on the coordinates of the feature points of the reference projection image, that is, 1.5 times the size of the sub-area of the reference projection image, record the correlation coefficient and the coordinates of the feature points, and obtain rough matching points ;
步骤314,在96×96区域搜索完成后,比较所有记录的粗匹配点的相关系数,选出最大的相关系数,进行阈值TNCC限定;Step 314, after the 96×96 area search is completed, compare the correlation coefficients of all recorded rough matching points, select the largest correlation coefficient, and limit the threshold T NCC ;
步骤315,如果大于给定阈值TNCC,则相应坐标点作为中间图像的特征点和参考投影图特征点的精匹配点;Step 315, if it is greater than the given threshold T NCC , then the corresponding coordinate point is used as the fine matching point between the feature point of the intermediate image and the feature point of the reference projection image;
步骤316,根据精匹配点对用最小二乘法进行拟合;Step 316, fitting with the least squares method according to the fine matching points;
步骤317,求出中间图像和参考投影图间的变换矩阵,得到投影匹配的最后变换参数,完成投影图匹配。In step 317, the transformation matrix between the intermediate image and the reference projection image is obtained, and the final transformation parameters for projection matching are obtained to complete the projection image matching.
所述步骤4,包括如下步骤:Said step 4 comprises the following steps:
步骤41,获取投影匹配完成后得到的变换参数;Step 41, obtaining the transformation parameters obtained after the projection matching is completed;
步骤42,返回到三维地形中,对三维地形进行转换;Step 42, return to the 3D terrain, and convert the 3D terrain;
步骤43,完成三维地形匹配。Step 43, complete the three-dimensional terrain matching.
该方法包括三维DEM(Digital Elevation Model)地形数据的获取与转换,对三维地形进行正射投影,根据地形的正射投影再进行投影图匹配。对投影图进行匹配过程中采用直线特征与点特征结合的方法,在对投影图进行直线检测并匹配完同名直线之后,找到同名直线两两相交的虚拟角点,并计算出该角点的坐标。然后利用改进的SURF算法对找出的虚拟角点进行匹配,当且仅当对应的角点匹配时,才认为这两条直线正确匹配。计算出投影图之间的变换关系,再将变换参数应用于三维地形之间的匹配,从而完成整个地形匹配过程。本发明利用三维地形投影进行匹配,是一种三维地形匹配的新方法。本发明可以应用于无人机视觉导航中,退化环境下,有遮挡的情况,针对异构的投影进行匹配。The method includes acquisition and conversion of 3D DEM (Digital Elevation Model) terrain data, orthographic projection of the 3D terrain, and matching of projection maps according to the orthographic projection of the terrain. In the process of matching the projection image, the method of combining the straight line feature and the point feature is adopted. After the line detection is performed on the projection image and the straight line with the same name is matched, the virtual corner point where the two straight lines with the same name intersect is found, and the coordinates of the corner point are calculated. . Then use the improved SURF algorithm to match the found virtual corners. If and only when the corresponding corners match, the two straight lines are considered to match correctly. Calculate the transformation relationship between the projection maps, and then apply the transformation parameters to the matching between the three-dimensional terrain, so as to complete the whole terrain matching process. The invention uses three-dimensional terrain projection for matching, and is a new method for three-dimensional terrain matching. The present invention can be applied to unmanned aerial vehicle visual navigation, under the condition of degraded environment, there is occlusion, and is aimed at the matching of heterogeneous projection.
本发明的有益效果是:充分利用地形的表面特征,对三维地形的正射投影进行匹配,提供了一种新的三维地形匹配算法。对于异构的地形投影,该算法也适用。改进的SURF点特征算法提高了投影匹配的精度。The beneficial effect of the invention is that the surface features of the terrain are fully utilized to match the orthographic projection of the three-dimensional terrain, and a new three-dimensional terrain matching algorithm is provided. The algorithm also works for heterogeneous terrain projections. The improved SURF point feature algorithm improves the accuracy of projection matching.
附图说明Description of drawings
图1本发明流程图;Fig. 1 flow chart of the present invention;
图2地形数据格式转换流程图;Fig. 2 flow chart of terrain data format conversion;
图3地形投影图获取;Fig. 3 acquisition of terrain projection map;
图4改进SURF算法流程图;Fig. 4 improves the SURF algorithm flow chart;
图5地形匹配过程;Figure 5 Terrain matching process;
图6异构投影图的SURF与改进SURF算法结果。Figure 6 The results of SURF and improved SURF algorithm for heterogeneous projection graphs.
具体实施方式Detailed ways
如图1所示,三维地形匹配的流程图步骤特征是:As shown in Figure 1, the characteristics of the flowchart steps of 3D terrain matching are:
步骤1,将原始数字高程模型地形数据格式USGS-DEM转换为数字高程模型地形数据格式CNSTDF-DEM;Step 1, convert the original digital elevation model terrain data format USGS-DEM into digital elevation model terrain data format CNSTDF-DEM;
步骤2,对步骤1所述地形格式的参考三维地形和待匹配三维地形进行正射投影;Step 2, performing orthographic projection on the reference 3D terrain of the terrain format described in step 1 and the 3D terrain to be matched;
步骤3,根据步骤2所述的正射投影,对投影图进行匹配,得到投影图变换参数,匹配过程采用直线特征和点特征结合的方法;Step 3, according to the orthographic projection described in step 2, the projection map is matched to obtain the projection map transformation parameters, and the matching process adopts the method of combining line features and point features;
步骤4,根据步骤3所述的变换参数,对参考三维地形和待匹配三维地形进行匹配并得到匹配结果。Step 4, according to the transformation parameters described in step 3, match the reference 3D terrain and the 3D terrain to be matched and obtain a matching result.
如图2所示,所述步骤1,包括如下步骤,其特征是:As shown in Figure 2, said step 1 includes the following steps, which are characterized in that:
步骤11,打开一个地形文件;Step 11, open a terrain file;
步骤12,判断打开的文件是否为USGS-DEM地形数据格式;Step 12, judging whether the opened file is in the USGS-DEM terrain data format;
步骤13,如果不是则转到步骤11,如果是则开始读取数据头;Step 13, if not then go to step 11, if it is then start to read the data header;
步骤14,提取相关地形文件头信息;Step 14, extracting relevant terrain file header information;
步骤15,存储文件头信息;Step 15, storing file header information;
步骤16,开辟数据存储空间;Step 16, open up data storage space;
步骤17,读取数据体;Step 17, read the data body;
步骤18,过滤数据体的每行的前144个字节;Step 18, filtering the first 144 bytes of each line of the data body;
步骤19,存储数据相关信息;Step 19, storing data-related information;
步骤110,结合存储的文件头和数据体保存为CNSTDF-DEM地形格式文件;Step 110, save as CNSTDF-DEM terrain format file in conjunction with stored file header and data body;
步骤111,地形转换完成。Step 111, terrain conversion is completed.
如图3所示,所述步骤2,包括如下步骤,其特征是:As shown in Figure 3, said step 2 includes the following steps, which are characterized in that:
步骤21,打开得到的三维CNSTDF-DEM地形数据;Step 21, open the obtained three-dimensional CNSTDF-DEM terrain data;
步骤22,得到地形表面纹理特征;Step 22, obtaining terrain surface texture features;
步骤23,得到三维地形纹理;Step 23, obtaining a three-dimensional terrain texture;
步骤24,根据纹理得到地形正射投影图。In step 24, the terrain orthographic projection map is obtained according to the texture.
如图4所示,所述步骤3,包括如下步骤,其特征是:As shown in Figure 4, said step 3 includes the following steps, characterized in that:
步骤31,对待匹配投影图和参考投影图进行直线的检测与匹配,匹配完同名直线后,在待匹配投影图中将两两直线相交的交点作为待匹配投影图虚拟角点;Step 31, performing line detection and matching on the projection image to be matched and the reference projection image, after matching the straight lines with the same name, using the intersection point where two straight lines intersect in the projection image to be matched as the virtual corner point of the projection image to be matched;
步骤32,在参考投影图中将两两直线相交的交点作为参考投影图虚拟角点。Step 32, taking the intersection point where two lines intersect in the reference projected map as the virtual corner point of the reference projected map.
步骤33,对虚拟角点进行SURF算法粗匹配;Step 33, performing rough matching of SURF algorithm on virtual corner points;
步骤34,对虚拟角点进行SURF算法精匹配;Step 34, performing SURF algorithm fine matching on the virtual corner points;
步骤35,通过第一次匹配,得到了参考图像和待配准图像的精确特征点对集合φAB,通过φAB便可以求取参考图像和待配准图像间的透视变换矩阵H;Step 35, through the first matching, the accurate feature point pair set φ AB of the reference image and the image to be registered is obtained, and the perspective transformation matrix H between the reference image and the image to be registered can be obtained through φ AB ;
步骤36,对待匹配投影图根据求出的变换矩阵H进行变换;Step 36, transforming the projection image to be matched according to the obtained transformation matrix H;
步骤37,对待匹配投影图进行插值;Step 37, interpolating the projection image to be matched;
步骤38,变换插值之后得到中间图像;Step 38, obtain the intermediate image after transformation and interpolation;
步骤39,获得参考投影图和待匹配投影图;Step 39, obtaining the reference projection image and the projection image to be matched;
步骤310,找出参考投影图和待匹配投影图之间的重叠部分作为各自的感兴趣区域,并且根据重叠区域大小划分参考图像为多块子区域。当重叠区域较大时,子区域大小为64×64,当重叠区域较小时子区域也相应的变小;Step 310, find out the overlapping parts between the reference projection image and the projection image to be matched as respective regions of interest, and divide the reference image into multiple sub-regions according to the size of the overlapping regions. When the overlapping area is larger, the size of the sub-area is 64×64, and when the overlapping area is smaller, the sub-area becomes smaller accordingly;
步骤311,在参考投影图的子区域中,以区域中心为中心,在周围32×32,即参考图像子区域大小的0.5倍邻域内进行HARRIS特征点提取,取该区域中所有HARRIS特征点中R值最大的,即与周围点最有区分度的点作为参考投影图的特征点;如果在32×32邻域内没有HARRIS特征点,则把子区域中心作为一个特征点处理;Step 311, in the sub-region of the reference projection image, take the center of the region as the center, and perform HARRIS feature point extraction in a neighborhood of 32×32, that is, 0.5 times the size of the reference image sub-region, and take all the HARRIS feature points in this region The point with the largest R value, that is, the most distinguishable point from the surrounding points is used as the feature point of the reference projection map; if there is no HARRIS feature point in the 32×32 neighborhood, the center of the sub-region is treated as a feature point;
步骤312,当所有的参考投影图特征点提取完后,进行NCC算法匹配;Step 312, after all the reference projection map feature points are extracted, perform NCC algorithm matching;
步骤313,在中间图像中,以参考投影图特征点坐标为中心的96×96(参考投影图子区域大小的1.5倍)区域内进行搜索,记录相关系数和其特征点坐标,得到粗匹配点;Step 313, in the intermediate image, search in the 96×96 (1.5 times the size of the sub-area of the reference projection image) area centered on the coordinates of the feature points of the reference projection image, record the correlation coefficient and the coordinates of the feature points, and obtain rough matching points ;
步骤314,在96×96区域搜索完成后,比较所有记录的粗匹配点的相关系数,选出最大的的相关系数,进行阈值TNCC限定;Step 314, after the 96×96 area search is completed, compare the correlation coefficients of all recorded rough matching points, select the largest correlation coefficient, and limit the threshold T NCC ;
步骤315,如果大于给定阈值TNCC,则相应坐标点作为中间图像的特征点和参考投影图特征点的精匹配点。Step 315, if it is greater than a given threshold T NCC , the corresponding coordinate point is used as a precise matching point between the feature point of the intermediate image and the feature point of the reference projection image.
步骤316,根据精匹配点对用最小二乘法进行拟合;Step 316, fitting with the least squares method according to the fine matching points;
步骤317,求出中间图像和参考投影图间的变换矩阵,得到投影匹配的最后变换参数,完成投影图匹配。In step 317, the transformation matrix between the intermediate image and the reference projection image is obtained, and the final transformation parameters for projection matching are obtained to complete the projection image matching.
如图5所示,所述步骤4,包括如下步骤,其特征是:As shown in Figure 5, said step 4 includes the following steps, which are characterized in that:
步骤41,获取投影匹配完成后得到的变换参数;Step 41, obtaining the transformation parameters obtained after the projection matching is completed;
步骤42,返回到三维地形中,对三维地形进行转换;Step 42, return to the 3D terrain, and convert the 3D terrain;
步骤43,完成三维地形匹配。Step 43, complete the three-dimensional terrain matching.
如图6所示,图6(a)为地形的可见光投影图,图6(b)为地形的红外投影图,图6(c)为用SURF算法进行匹配的结果,匹配精度为0.2108像素,图6(d)为用本文改进SURF算法进行匹配的结果,匹配精度为0.0338像素。结果表明改进SURF算法提高了匹配过程的精度。As shown in Figure 6, Figure 6(a) is the visible light projection map of the terrain, Figure 6(b) is the infrared projection map of the terrain, Figure 6(c) is the matching result using the SURF algorithm, and the matching accuracy is 0.2108 pixels, Figure 6(d) is the matching result using the improved SURF algorithm in this paper, and the matching accuracy is 0.0338 pixels. The results show that the improved SURF algorithm improves the accuracy of the matching process.
步骤中没有详细叙述的部分属本领域公知的常用手段及算法,这里不一一叙述。The parts not described in detail in the steps belong to common means and algorithms known in the art, and will not be described here one by one.
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