CN108550174A - A kind of coastline Super-resolution Mapping and system based on half global optimization - Google Patents
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Abstract
本发明提出了一个基于半全局优化的海岸线超分辨率制图方法及系统。所述方法主要包含:获取初始海岸线影像与参考影像,进行图像预处理和图像融合及影像配准,将整体海岸线形态的变化趋势和初始海岸线周边的灰度变化相结合,提取出海岸线变化控制点,并用其将初始海岸线分为若干海岸线段;在每一段中,在海岸线邻域窗口内,获得亚像元定位结果,并结合同一段内所有点的亚像元定位坐标,将其拟合为一条平滑的曲线段;将所有曲线段组合在一起即为完整的海岸线矢量结果,完成海岸线的超分辨率制图;本发明提出了一种基于局部区域的亚像元定位方法和能适应高曲率的海岸线环境,定位精度高,可以适应不同曲率的海岸线,提高了结果的准确性。
The present invention proposes a coastline super-resolution mapping method and system based on semi-global optimization. The method mainly includes: acquiring an initial coastline image and a reference image, performing image preprocessing, image fusion and image registration, combining the change trend of the overall coastline shape with the gray level change around the initial coastline, and extracting coastline change control points , and use it to divide the initial coastline into several coastline segments; in each segment, in the coastline neighborhood window, obtain the sub-pixel positioning results, and combine the sub-pixel positioning coordinates of all points in the same segment to fit it as A smooth curve segment; combining all the curve segments together is the complete coastline vector result, and completes the super-resolution mapping of the coastline; the invention proposes a sub-pixel positioning method based on local areas and can adapt to high curvature The coastline environment has high positioning accuracy and can adapt to coastlines with different curvatures, improving the accuracy of the results.
Description
技术领域technical field
本发明属于海岸线图像处理领域,涉及到一种基于半全局优化的海岸线超分辨率制图 方法及系统。The invention belongs to the field of coastline image processing, and relates to a coastline super-resolution mapping method and system based on semi-global optimization.
背景技术Background technique
海岸线是海洋与陆地的分界线,是最重要的27种地表特征之一。海岸线受自然因素及 人为因素的影响,产生了形态和性质的变化。海水动力作用(包括潮汐潮流等)、地质气象 灾害、气候变暖导致的海平面上升等自然条件的变化导致海岸淤进、蚀退;人类围垦、填 海造地、海洋工程等人类活动的影响,导致海岸线类型转变的同时海岸线的走向和长度也 发生了明显的改变。The coastline is the dividing line between the sea and the land, and is one of the 27 most important land surface features. Affected by natural factors and human factors, the coastline has undergone changes in shape and nature. Changes in natural conditions such as seawater dynamics (including tidal currents, etc.), geological and meteorological disasters, and sea level rise caused by climate warming lead to coastal silting and erosion; impacts of human activities such as reclamation, land reclamation, and marine engineering, The trend and length of the coastline have also changed significantly while leading to the transformation of the coastline type.
因此,海岸线测绘和变化检测已是海岸侵蚀监测,海岸带资源管理,沿海地区环境保 护和沿海地区可持续发展的基础性工作。我国海洋资源丰富,海岸线利用程度高,研究海 岸线的快速准确提取有利用我国的海岸带综合规划治理、资源的合理开发利用,对于我国 的社会、经济与自然的可持续发展具有重要的现实意义。海岸线提取的主要方法有图像分 割方法和边缘检测方法。图像分割方法是将影像数据分为前景和背景两部分,其交界处即 为提取的海岸线。Therefore, coastline mapping and change detection have become the basic work of coastal erosion monitoring, coastal resource management, environmental protection of coastal areas and sustainable development of coastal areas. my country is rich in marine resources and has a high degree of coastline utilization. Research on the rapid and accurate extraction of coastlines is of great practical significance for the sustainable development of society, economy and nature in my country. The main methods of coastline extraction are image segmentation method and edge detection method. The image segmentation method is to divide the image data into two parts, foreground and background, and the junction of them is the extracted coastline.
图像分割算法有很多,如利用水平集模型从SAR图像中提取海岸线(Cerimele etal. 2009;Ouyang,Chong,and Wu.2010;Shu,Li,and Gomes.2010);基于几何主动轮廓模型提取 海岸线(Niedermeier,Romaneessen,and Lehner.2000;Xing,Fu,and Zhou.2012;Zhang et al. 2013);结合图像分割方法和影像局部特征从高分辨率影像上提取海岸线(Di et al.2003;Liu et al.2011);整合有监督和无监督的分类方法用于海岸线提取(Sekovski et al.2014)。There are many image segmentation algorithms, such as using level set model to extract coastline from SAR image (Cerimele et al. 2009; Ouyang, Chong, and Wu.2010; Shu, Li, and Gomes.2010); extracting coastline based on geometric active contour model ( Niedermeier, Romaneessen, and Lehner.2000; Xing, Fu, and Zhou.2012; Zhang et al. 2013); combining image segmentation methods and image local features to extract coastlines from high-resolution images (Di et al.2003; Liu et al. al.2011); integrating supervised and unsupervised classification methods for coastline extraction (Sekovski et al.2014).
边缘检测方法是通过搜索相邻像素之间的能量强度(亮度)不连续性来提取海岸线 (Buono et al.2014;Fugura,Billa,and Pradhan 2011;Zhang et al.2013b)。Edge detection methods extract coastlines by searching for energy intensity (brightness) discontinuities between adjacent pixels (Buono et al. 2014; Fugura, Billa, and Pradhan 2011; Zhang et al. 2013b).
其他方法包括使用数学形态学从高分辨率影像中提取海岸线(Puissant etal.2008);基 于摄影测量技术,利用数字航空照片和数字正射影像提取海岸线(Lira etal.2016);以及基 于遥感视频系统的自动检测海岸线(Valentini.2017)。Other methods include extracting coastlines from high-resolution images using mathematical morphology (Puissant et al.2008); based on photogrammetry technology, using digital aerial photos and digital orthophotos to extract coastlines (Lira et al. Automatic detection of coastlines (Valentini.2017).
到目前为止,大部分的研究都是找到一个确定海岸线位置的最佳方法。由于大多数方 法都是基于硬分类的,因此海岸线只有像元级的定位精度,不能满足实际精度的需求。部 分学者利用高分辨率遥感影像提取海岸线,虽然能得到高精度的海岸线,但是其昂贵的价 格无法满足实际需求中大范围海岸线的提取。因此需要一种利用中低分辨率遥感影像提取 高精度海岸线的方法。随着图像处理技术的发展,特别是亚像元定位和超分辨率重建技术 的发展,部分学者将其用在中低分辨率遥感影像的海岸线提取中,使提取的海岸线具有较 高的精度。So far, most of the research has been to find an optimal way to determine the location of the coastline. Since most methods are based on hard classification, the coastline only has pixel-level positioning accuracy, which cannot meet the actual accuracy requirements. Some scholars use high-resolution remote sensing images to extract coastlines. Although high-precision coastlines can be obtained, the high price cannot meet the actual needs of large-scale coastline extraction. Therefore, there is a need for a method for extracting high-precision coastlines from medium and low-resolution remote sensing images. With the development of image processing technology, especially the development of sub-pixel positioning and super-resolution reconstruction technology, some scholars use it in the coastline extraction of medium and low resolution remote sensing images, so that the extracted coastline has higher accuracy.
Foody等人(2005)从退化的模拟Landsat影像评估海岸线的软分类方法,结果呈现的 亚像元尺度的海岸线和RMSE是2.25米。Pardo-Pascual等人(2012)提出了一种自动的方法从Landsat TM和ETM+中提取亚像元精度的海岸线,海岸线位置的RMSE从4.69到5.47 m。由于自然海滩随时间而变化,Almonacid-Caballer等人(2016)应用从Landsat图像中 提取的年平均海岸线位置来大幅度减少海岸线的短期变化,而且提取的海岸线偏离海面约 4至5米。Foody et al. (2005) evaluated a soft classification method for coastlines from degraded simulated Landsat images, and the results showed sub-pixel-scale coastlines and an RMSE of 2.25 m. Pardo-Pascual et al. (2012) proposed an automatic method to extract coastlines with sub-pixel accuracy from Landsat TM and ETM+, and the RMSE of coastline positions ranged from 4.69 to 5.47 m. As natural beaches change over time, Almonacid-Caballer et al. (2016) applied the annual average coastline position extracted from Landsat images to substantially reduce the short-term variation of the coastline, and the extracted coastline was about 4 to 5 meters away from the sea surface.
这些方法在实际应用中往往是复杂而难以实现的,且具有一定的局限性,比如受岸边 悬浮泥沙的影响,无法适应高曲率的海岸线环境,无法去除停泊船只的影响等。These methods are often complex and difficult to implement in practical applications, and have certain limitations, such as being affected by the suspended sediment on the shore, unable to adapt to the high-curvature coastline environment, and unable to remove the influence of moored ships.
大多数方法都是基于硬分类的,因此海岸线只有像元级的定位精度,不能满足实际精 度的需求。在像元级海岸线提取的方法中,图像分割算法在提取海岸线时,为了保证提取 精度,需要更多的后处理步骤来确定边界像素和阈值大小;区域生长算法必须考虑很多标 准和阈值,包括分裂、合并和起点选择;基于边缘检测方法的缺陷是这些方法产生的不连 续线不能很好地代表海岸线。部分学者利用高分辨率遥感影像提取海岸线,虽然能得到高 精度的海岸线,但是其昂贵的价格无法满足实际需求中大范围海岸线的提取。因此需要一 种利用中低分辨率遥感影像提取高精度海岸线的方法。而软分类方法在实际应用中往往是 复杂而难以实现的,且具有一定的局限性,比如受岸边悬浮泥沙的影响,无法适应高曲率 的海岸线环境,无法去除停泊船只的影响等。Most methods are based on hard classification, so the coastline only has pixel-level positioning accuracy, which cannot meet the actual accuracy requirements. In the method of pixel-level coastline extraction, when the image segmentation algorithm extracts the coastline, in order to ensure the extraction accuracy, more post-processing steps are required to determine the boundary pixels and the threshold value; the region growing algorithm must consider many criteria and thresholds, including splitting , merging, and starting point selection; the drawback of edge detection-based methods is that the discontinuities produced by these methods do not represent coastlines well. Some scholars use high-resolution remote sensing images to extract coastlines. Although high-precision coastlines can be obtained, the high price cannot meet the actual needs of large-scale coastline extraction. Therefore, there is a need for a method for extracting high-precision coastlines using low- and medium-resolution remote sensing images. However, the soft classification method is often complicated and difficult to implement in practical applications, and has certain limitations, such as being affected by the suspended sediment on the shore, unable to adapt to the high curvature coastline environment, and unable to remove the influence of moored ships.
发明内容Contents of the invention
针对现有技术问题的缺点和不足,本发明提供了一种基于半全局优化的海岸线超分辨 率制图方法及系统,用于解决定位精度不高,无法适应高曲率的海岸线环境的技术问题。 所述方法主要包括如下步骤:Aiming at the shortcomings and deficiencies of the existing technical problems, the present invention provides a coastline super-resolution mapping method and system based on semi-global optimization, which is used to solve the technical problems that the positioning accuracy is not high and cannot adapt to the high-curvature coastline environment. Described method mainly comprises the steps:
S1、获取Landsat 8陆地成像仪影像和GF-2参考影像,分别对Landsat 8陆地成像仪影 像和GF-2参考影像进行图像预处理,获得Landsat 8融合影像、Landsat 8融合影像对应的 水体指数灰度图及GF-2号融合影像;S1. Obtain the Landsat 8 land imager image and the GF-2 reference image, perform image preprocessing on the Landsat 8 land imager image and the GF-2 reference image, respectively, and obtain the Landsat 8 fusion image and the water index gray corresponding to the Landsat 8 fusion image Degree map and fusion image of GF-2;
S2、将GF-2号融合影像作为参考影像,对所述参考影像和Landsat 8融合影像进行配 准,获得参考影像和Landsat 8融合影像之间的偏移参数;S2, No. GF-2 fusion image is used as reference image, registration is carried out to described reference image and Landsat 8 fusion image, obtains the offset parameter between reference image and Landsat 8 fusion image;
S3、对所述水体指数灰度图进行像元级海岸线提取,获得初始海岸线;对参考影像进行 海岸线提取,获得参考海岸线;S3. Perform pixel-level coastline extraction on the water body index grayscale image to obtain an initial coastline; perform coastline extraction on a reference image to obtain a reference coastline;
S4、对所述初始海岸线进行海岸线变化控制点提取,获得初始海岸线变化控制点,通 过初始海岸线变化控制点将所述初始海岸线分段,获得若干段分段海岸线;S4. Extracting coastline change control points from the initial coastline to obtain initial coastline change control points, and segmenting the initial coastline through the initial coastline change control points to obtain several segmented coastlines;
S5、对每一段海岸线中的每一个像元点进行基于局部区域的亚像元定位,获得每一段 海岸线中每个点的亚像元定位坐标;通过步骤S2所述的偏移参数对每个点的亚像元定位坐 标进行偏移纠正;S5. Carry out sub-pixel positioning based on the local area for each pixel point in each section of coastline, and obtain the sub-pixel positioning coordinates of each point in each section of coastline; through the offset parameters described in step S2 for each The sub-pixel positioning coordinates of the point are offset corrected;
S6、对同一段海岸线内所有点的已偏移纠正过的亚像元定位坐标进行海岸线最小二乘 拟合,将其拟合为一条平滑的曲线段;将所有曲线段组合在一起得到完整的海岸线矢量结 果,完成海岸线的超分辨率制图。S6. Carry out coastline least squares fitting to the offset-corrected sub-pixel positioning coordinates of all points in the same coastline, and fit it into a smooth curve segment; combine all curve segments together to obtain a complete Coastline vector results, complete super-resolution mapping of coastlines.
本发明的一种基于半全局优化的海岸线超分辨率制图方法中,还包括:分别计算所述 海岸线亚像元定位坐标与参考海岸线之间、海岸线矢量结果与参考海岸线之间的位置误差 并分析误差结果。In a coastline super-resolution mapping method based on semi-global optimization of the present invention, it also includes: respectively calculating and analyzing the position errors between the coastline sub-pixel positioning coordinates and the reference coastline, between the coastline vector result and the reference coastline error result.
本发明的一种基于半全局优化的海岸线超分辨率制图方法中,步骤S5中所述基于局部 区域的亚像元定位包含以下步骤:In a kind of coastline super-resolution mapping method based on semi-global optimization of the present invention, the sub-pixel positioning based on local area described in step S5 comprises the following steps:
S51、计算所述每一段初始海岸线中的每一个像元点的边缘方向,根据不同的边缘方向 确定初始海岸线每一个像元点不同的邻域窗口;S51. Calculate the edge direction of each pixel point in each section of the initial coastline, and determine different neighborhood windows for each pixel point of the initial coastline according to different edge directions;
S52、通过拟合函数,将所述领域窗口边界描述为一个曲线,计算拟合函数的参数,确 定局部区域的亚像元定位点的坐标。S52. Describe the domain window boundary as a curve through the fitting function, calculate the parameters of the fitting function, and determine the coordinates of the sub-pixel positioning points in the local area.
本发明的一种基于半全局优化的海岸线超分辨率制图方法中,步骤S6中所述海岸线最 小二乘拟合包含以下步骤:In a kind of coastline super-resolution mapping method based on semi-global optimization of the present invention, coastline least squares fitting described in step S6 comprises the following steps:
S61、检测并提取出每一段海岸线上的亚像元定位坐标;S61. Detect and extract the sub-pixel positioning coordinates of each section of coastline;
S62、将所述亚像元定位坐标分为不同的点集,对同一点集中的亚像元定位坐标进行最 小二乘拟合,得到亚像元级海岸线。S62. Divide the sub-pixel positioning coordinates into different point sets, and perform least squares fitting on the sub-pixel positioning coordinates in the same point set to obtain sub-pixel-level coastlines.
优选的,本发明还提供一种基于半全局优化的海岸线超分辨率制图系统,包括如下模 块:Preferably, the present invention also provides a coastline super-resolution mapping system based on semi-global optimization, including the following modules:
图像预处理图像融合模块,用于获取Landsat 8陆地成像仪影像和GF-2参考影像,分 别对Landsat 8陆地成像仪影像和GF-2参考影像进行图像预处理,获得Landsat 8融合影像、 Landsat 8融合影像对应的水体指数灰度图及GF-2号融合影像;The image preprocessing image fusion module is used to obtain Landsat 8 land imager images and GF-2 reference images, respectively perform image preprocessing on Landsat 8 land imager images and GF-2 reference images, and obtain Landsat 8 fusion images, Landsat 8 The grayscale image of the water body index corresponding to the fusion image and the fusion image of GF-2;
影像配准模块,用于将GF-2号融合影像作为参考影像,对所述参考影像和Landsat8 融合影像进行配准,获得参考影像和Landsat 8融合影像之间的偏移参数;The image registration module is used to use the GF-2 fusion image as a reference image, register the reference image and the Landsat8 fusion image, and obtain the offset parameters between the reference image and the Landsat 8 fusion image;
海岸线提取模块,用于对所述水体指数灰度图进行像元级海岸线提取,获得初始海岸 线;对参考影像进行海岸线提取,获得参考海岸线;The coastline extraction module is used to extract the pixel-level coastline from the water body index grayscale image to obtain the initial coastline; to carry out the coastline extraction to the reference image to obtain the reference coastline;
海岸线变化控制点提取,用于对所述初始海岸线进行海岸线变化控制点提取,获得初 始海岸线变化控制点,通过初始海岸线变化控制点将所述初始海岸线分段,获得若干段分 段海岸线;The coastline change control point extraction is used to extract the coastline change control point for the initial coastline to obtain the initial coastline change control point, and the initial coastline is segmented by the initial coastline change control point to obtain several segmented coastlines;
亚像元定位模块,用于对每一段海岸线中的每一个像元点进行基于局部区域的亚像元 定位,获得每一段海岸线中每个点的亚像元定位坐标;通过影像配准模块所述的偏移参数 对每个点的亚像元定位坐标进行偏移纠正;The sub-pixel positioning module is used to perform sub-pixel positioning based on the local area for each pixel point in each section of coastline, and obtain the sub-pixel positioning coordinates of each point in each section of coastline; through the image registration module The above-mentioned offset parameters are used to correct the offset of the sub-pixel positioning coordinates of each point;
定位点拟合模块,用于对同一段海岸线内所有点的已偏移纠正过的亚像元定位坐标进 行海岸线最小二乘拟合,将其拟合为一条平滑的曲线段;将所有曲线段组合在一起得到完 整的海岸线矢量结果,完成海岸线的超分辨率制图。The positioning point fitting module is used to perform coastline least squares fitting on the offset-corrected sub-pixel positioning coordinates of all points in the same section of coastline, and fit it into a smooth curve segment; Combined to get a complete coastline vector result, complete the super-resolution mapping of the coastline.
本发明的一种基于半全局优化的海岸线超分辨率制图系统中,还包括误差分析模块: 用于分别计算所述海岸线亚像元定位坐标与参考海岸线之间、海岸线矢量结果与参考海岸 线之间的位置误差并分析误差结果。In the coastline super-resolution mapping system based on semi-global optimization of the present invention, an error analysis module is also included: for calculating the distance between the coastline sub-pixel positioning coordinates and the reference coastline, and the distance between the coastline vector result and the reference coastline. position error and analyze the error results.
本发明的一种基于半全局优化的海岸线超分辨率制图系统中,亚像元定位模块中所述 基于局部区域的亚像元定位包含以下模块:In a coastline super-resolution mapping system based on semi-global optimization of the present invention, the sub-pixel positioning based on the local area described in the sub-pixel positioning module includes the following modules:
邻域窗口确定模块,用于计算所述每一段初始海岸线中的每一个像元点的边缘方向, 根据不同的边缘方向确定初始海岸线每一个像元点不同的邻域窗口;The neighborhood window determination module is used to calculate the edge direction of each pixel point in each section of the initial coastline, and determine the different neighborhood windows of each pixel point of the initial coastline according to different edge directions;
亚像元定位点坐标获取模块,用于通过拟合函数,将所述领域窗口边界描述为一个曲 线,计算拟合函数的参数,确定局部区域的亚像元定位点的坐标。The sub-pixel anchor point coordinate acquisition module is used to describe the domain window boundary as a curve through the fitting function, calculate the parameters of the fitting function, and determine the coordinates of the sub-pixel anchor point in the local area.
本发明的一种基于半全局优化的海岸线超分辨率制图系统中,定位点拟合模块中所述 海岸线最小二乘拟合包含以下模块:In a kind of coastline super-resolution mapping system based on semi-global optimization of the present invention, the coastline least squares fitting described in the positioning point fitting module includes the following modules:
提取亚像元定位坐标模块,用于检测并提取出每一段海岸线上的亚像元定位坐标;Extract sub-pixel positioning coordinates module, which is used to detect and extract sub-pixel positioning coordinates on each coastline;
亚像元级海岸线获取模块,用于将所述亚像元定位坐标分为不同的点集,对同一点集 中的亚像元定位坐标进行最小二乘拟合,得到亚像元级海岸线。The sub-pixel level coastline acquisition module is used to divide the sub-pixel positioning coordinates into different point sets, and carry out least squares fitting to the sub-pixel positioning coordinates in the same point set to obtain the sub-pixel level coastline.
本发明提出了一种基于半全局优化的海岸线超分辨率制图方法及系统,获取初始海岸 线影像与参考影像,进行图像预处理和图像融合及影像配准,将整体海岸线形态的变化趋 势和初始海岸线周边的灰度变化相结合,提取出海岸线变化控制点,并用其将初始海岸线 分为若干海岸线段;在每一段中,在海岸线邻域窗口内,获得亚像元定位结果,并结合同 一段内所有点的亚像元定位坐标,将其拟合为一条平滑的曲线段;将所有曲线段组合在一 起即为完整的海岸线矢量结果,完成海岸线的超分辨率制图;本发明提出了一种基于局部 区域的亚像元定位方法和能适应高曲率的海岸线环境,定位精度高,可以适应不同曲率的 海岸线,提高了结果的准确性。The present invention proposes a coastline super-resolution mapping method and system based on semi-global optimization, which acquires initial coastline images and reference images, performs image preprocessing, image fusion and image registration, and integrates the change trend of the overall coastline form with the initial coastline Combined with the surrounding gray level changes, the coastline change control points are extracted, and the initial coastline is divided into several coastline segments; in each segment, the sub-pixel positioning results are obtained in the coastline neighborhood window, and combined with the same segment The sub-pixel positioning coordinates of all points are fitted into a smooth curve segment; all curve segments are combined together to be a complete coastline vector result, and the super-resolution mapping of the coastline is completed; the present invention proposes a method based on The sub-pixel positioning method in the local area can adapt to the coastline environment with high curvature, and has high positioning accuracy, and can adapt to coastlines with different curvatures, which improves the accuracy of the results.
附图说明Description of drawings
下面将结合附图及实施例对本发明作进一步说明,附图中:The present invention will be further described below in conjunction with accompanying drawing and embodiment, in the accompanying drawing:
图1为本发明实施例方法流程图;Fig. 1 is the flow chart of the method of the embodiment of the present invention;
图2为本发明实施例实验区1影像图;Fig. 2 is the image map of the experimental area 1 of the embodiment of the present invention;
图3为本发明实施例实验区2影像图;Fig. 3 is the image map of experimental area 2 of the embodiment of the present invention;
图4为本发明实施例初始海岸线提取图;Fig. 4 is the initial coastline extraction figure of the embodiment of the present invention;
图5为本发明实施例海岸线拐点提取示意图;Fig. 5 is a schematic diagram of extracting coastline inflection points according to an embodiment of the present invention;
图6为本发明实施例亚像元定位结果图;Fig. 6 is a sub-pixel positioning result diagram according to an embodiment of the present invention;
图7为本发明实施例分段拟合图;Fig. 7 is the subsection fitting diagram of the embodiment of the present invention;
图8为本发明实施例实验区1结果图;Fig. 8 is the result figure of experimental zone 1 of the embodiment of the present invention;
图9为本发明实施例实验区2结果图。Fig. 9 is a graph showing the results of the experimental area 2 of the embodiment of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实例,对本发 明进行进一步详细说明。In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings and examples.
本发明提出了一种基于半全局优化的海岸线超分辨率制图方法及系统,所述方法完整 流程图见图1,本发明实施例研究区域分为两大部分:曹妃甸港和厦门-泉州周边沿海区 域。曹妃甸港位于118.5°E,39°N附近,毗邻中国京津冀城市群,是中国重要的矿石运 输港口之一。曹妃甸过去只是一个沙岛,经人工建设后成为港口,因此其主要岸线类型为 人工海岸,海岸线位置长期稳定,多年未被改变。厦门-泉州周边实验区位于118°E -118.5°E和24.35°N-24.6°N之间,紧邻台湾海峡,厦门是国际经济和文化交流的重要 港口。与世界日益紧密的联系,推动了厦门和泉州周边沿海发展,导致近几十年来海岸线 地位迅速变化。厦门与泉州周边的海岸线类型主要有基岩海岸,人工海岸和平坦沙质海 岸。The present invention proposes a coastline super-resolution mapping method and system based on semi-global optimization. The complete flow chart of the method is shown in Figure 1. The research area of the embodiment of the present invention is divided into two parts: Caofeidian Port and Xiamen-Quanzhou surrounding coastal areas area. Caofeidian Port is located near 118.5°E, 39°N, adjacent to China's Beijing-Tianjin-Hebei urban agglomeration, and is one of China's important ore transportation ports. Caofeidian was just a sand island in the past, but it became a port after artificial construction. Therefore, its main coastline type is artificial coastline, and the coastline position has been stable for a long time and has not been changed for many years. The Xiamen-Quanzhou surrounding experimental area is located between 118°E-118.5°E and 24.35°N-24.6°N, adjacent to the Taiwan Strait, and Xiamen is an important port for international economic and cultural exchanges. The increasingly close connection with the world has promoted the coastal development around Xiamen and Quanzhou, resulting in rapid changes in the status of the coastline in recent decades. The coastline types around Xiamen and Quanzhou mainly include bedrock coast, artificial coast and flat sandy coast.
在发明实施例研究中,所有实验影像都是从USGS数据库下载,影像由Landsat8陆地 成像仪传感器获取。Landsat-8陆地成像仪影像使用WGS84椭球体模型,采用横轴墨卡托(UTM)投影,Landsat-8陆地成像仪影像的详细参数如表1所示。表1总结了每个日期, 每个实验区域和每个数据类型的误差统计(平均误差,标准偏差)。平均误差是通过对所 有误差进行平均而得到的,因为所有误差都是通过计算从最终海岸线点到参考海岸线的距 离的绝对值而获得的,所以使用平均误差来解释对参考海岸线的偏差水平。标准偏差 (STDEV)表示平均误差周围的变异性。In the research of the embodiment of the invention, all experimental images are downloaded from the USGS database, and the images are acquired by the Landsat8 land imager sensor. The Landsat-8 land imager images use the WGS84 ellipsoid model and the Transverse Mercator (UTM) projection. The detailed parameters of the Landsat-8 land imager images are shown in Table 1. Table 1 summarizes the error statistics (mean error, standard deviation) for each date, each experimental region, and each data type. The mean error is obtained by averaging all the errors, and since all errors are obtained by calculating the absolute value of the distance from the final shoreline point to the reference shoreline, the mean error is used to account for the level of deviation from the reference shoreline. The standard deviation (STDEV) represents the variability around the mean error.
表1 Landsat-8陆地成像仪参数表Table 1 Landsat-8 land imager parameter list
曹妃甸港位于122行-033列区块内,我们对2013年至2016年期间的12幅该区域影像进行处理,从每幅影像中选取了三个拥有不同水土分布的人工海岸线作为实验区1,用于确定抗噪性最佳的波段组合方式,见图2所示。厦门-泉州周边沿海区域从119行-043 列区块中获取,影像获取日期为2015年10月13日上午2点33分(UTC),从影像中选 取5块具有不同曲率的海岸线,见图3所示,用于验证算法的普适性。Caofeidian Port is located in block 122-033. We processed 12 images of this area from 2013 to 2016, and selected three artificial coastlines with different water and soil distributions from each image as the experimental area 1. It is used to determine the band combination method with the best noise immunity, as shown in Figure 2. The coastal area around Xiamen-Quanzhou was acquired from the block 119-043, and the image was acquired at 2:33 am on October 13, 2015 (UTC). Five coastlines with different curvatures were selected from the image, as shown in the figure 3, used to verify the universality of the algorithm.
此外,使用高分2号光学遥感卫星(GF-2)影像提取覆盖研究区域的参考海岸线,如图所示。高分二号卫星于2014年8月19日成功发射,是中国首颗空间分辨率优于1米的 民用光学遥感卫星,搭载有两台高分辨率1米全色、4米多光谱相机,具有亚米级空间分 辨率、高定位精度和快速姿态机动能力等特点。因为曹妃甸港内三个实验区均为人工海岸 线,且位置长期稳定,不受海潮影响,因此使用2015年05月31日获取的GF-2号影像作 为参考影像。而厦门-泉州周边沿海区域内存在沙质海岸和基岩海岸,易受海潮影响,因 此选取接近实验区获取时间的高分2号影像作为参考影像,以消除或减少潮汐对结果精度 的影响。由于高分2号影像的空间分辨率为1米/像素,所以海岸线参考位置的不确定度 估计为±1.5米。表1列出了有关GF-2影像的更多信息。In addition, the Gaofen-2 optical remote sensing satellite (GF-2) image was used to extract the reference coastline covering the study area, as shown in the figure. The Gaofen-2 satellite was successfully launched on August 19, 2014. It is China's first civilian optical remote sensing satellite with a spatial resolution better than 1 meter. It is equipped with two high-resolution 1-meter panchromatic and 4-meter multispectral cameras. It has the characteristics of sub-meter spatial resolution, high positioning accuracy and fast attitude maneuverability. Because the three experimental areas in Caofeidian Port are all artificial coastlines, and their positions are stable for a long time without being affected by tides, the GF-2 image acquired on May 31, 2015 was used as a reference image. However, there are sandy coasts and bedrock coasts in the coastal area around Xiamen-Quanzhou, which are easily affected by sea tides. Therefore, the Gaofen-2 image close to the acquisition time of the experimental area was selected as a reference image to eliminate or reduce the influence of tides on the accuracy of the results. Since the spatial resolution of the GF-2 image is 1 m/pixel, the uncertainty of the coastline reference position is estimated to be ±1.5 m. Table 1 lists more information about GF-2 images.
1.图像预处理。因为传感器自身、大气、地形等原因,遥感卫星在数据获取过程中会 产生误差,图像质量受到误差影响,从而影响图像精度,因此需要在实验前分别对Landsat 8 OLI实验影像和GF-2参考影像进行图像预处理。首先利用GF-2号影像数据自 带的RPC参数,分别对GF-2影像多光谱数据和全色数据进行正射校正。由于高分辨率数据 正射校正后多光谱和全色数据地理配准较好,因此不必再进行图像配准,直接采用 NearestNeighbor Diffusion(NNDiffuse)pan sharpening算法对GF-2号参考影像进 行图像融合,融合后GF-2号参考影像的空间分辨率为1m。再采用Nearest Neighbor Diffusion(NNDiffuse)pan sharpening算法对Landsat 8 OLI实验影像进行图像融合, 融合后Landsat 8 OLI实验影像的空间分辨率为15m。更小的采样间隔可以在相同长度的 海岸线上获得更多的像元,更多的初步海岸线像元有利于梯度的计算和亚像元定位结果的 准确性,因为邻域窗口面积越小,就越符合地物临近原理。然后,通过辐射定标参数对 Landsat8 OLI实验影像进行辐射定标,得到辐射亮度值数据。再利用FLAASH模型对辐射 亮度值数据进行大气校正(对Landsat 8 OLI实验影像进行快速大气校正),消除大气散 射对光谱的影响。1. Image preprocessing. Due to the sensor itself, the atmosphere, terrain and other reasons, the remote sensing satellite will produce errors in the data acquisition process, and the image quality will be affected by the errors, thereby affecting the image accuracy. Therefore, it is necessary to analyze the Landsat 8 OLI experimental image and the GF-2 reference image separately before the experiment. Perform image preprocessing. Firstly, the multispectral data and panchromatic data of GF-2 image were orthorectified respectively by using the RPC parameters of GF-2 image data. Since the georeferencing of multispectral and panchromatic data is better after orthorectification of high-resolution data, image registration is unnecessary, and the NearestNeighbor Diffusion (NNDiffuse) pan sharpening algorithm is directly used for image fusion of the GF-2 reference image. The spatial resolution of the fused GF-2 reference image is 1m. Next, the Nearest Neighbor Diffusion (NNDiffuse) pan sharpening algorithm was used to fuse the Landsat 8 OLI experimental images. After fusion, the spatial resolution of the Landsat 8 OLI experimental images was 15m. A smaller sampling interval can obtain more pixels on the coastline of the same length, and more preliminary coastline pixels are beneficial to the calculation of the gradient and the accuracy of the sub-pixel positioning results, because the smaller the neighborhood window area, the The more in line with the proximity principle of ground objects. Then, the Landsat8 OLI experiment image is radiometrically calibrated by radiometric calibration parameters to obtain radiance value data. Then use the FLAASH model to perform atmospheric correction on the radiance value data (fast atmospheric correction to the Landsat 8 OLI experiment image) to eliminate the influence of atmospheric scattering on the spectrum.
本发明充分利用多光谱遥感数据的光谱特征,分别计算归一化差分水体指数(NDWI) 和改进的归一化差异水体指数(MNDWI),具体表达式见公式1、公式2。这两种水体指数 均可以抑制图像噪声,而且其图像直方图为双峰直方图,满足后续算法的初始假设。在考 虑到岸边悬浮泥沙对结果的影响,选择短波红外(SWIR)波段作为另一个初始影像,因为短波红外(SWIR)波长较长,可以穿透悬浮泥沙。这些选择与其他类似研究的作者是一致的。The present invention makes full use of the spectral features of the multi-spectral remote sensing data to calculate the normalized difference water index (NDWI) and the improved normalized difference water index (MNDWI). The specific expressions are shown in formula 1 and formula 2. Both water indices can suppress image noise, and their image histograms are bimodal histograms, which meet the initial assumptions of subsequent algorithms. Considering the impact of suspended sediment on the shore, the short-wave infrared (SWIR) band was selected as another initial image, because the short-wave infrared (SWIR) has a longer wavelength and can penetrate suspended sediment. These choices are consistent with authors of other similar studies.
NDWI=(p(Green)-p(NIR))/(p(Green)+p(NIR)) (式1)NDWI=(p(Green)-p(NIR))/(p(Green)+p(NIR)) (Formula 1)
MNDWI=(p(Green)-p(MIR))/(p(Green)+p(MIR)) (式2)MNDWI=(p(Green)-p(MIR))/(p(Green)+p(MIR)) (Formula 2)
2.影像配准。计算Landsat 8融合影像与GF-2号融合影像(参考影像)之间的几何位 移。由于Landsat 8影像与GF-2号融合影像(参考影像)的影像分辨率不相同,因此选用具有尺度不变性的sift算法进行图像配准,以获取Landsat 8影像与参考影像之间的几何位移(dx,dy)。2. Image registration. Calculate the geometric displacement between Landsat 8 fused image and GF-2 fused image (reference image). Since the Landsat 8 image and the GF-2 fusion image (reference image) have different image resolutions, the scale-invariant SIFT algorithm is used for image registration to obtain the geometric displacement between the Landsat 8 image and the reference image ( dx, dy).
3.海岸线提取。提取示意图见图4,由于水和土地在不同波段的光谱响应不同,三种 不同波段组合图像的直方图呈现双峰性,满足OTSU算法的初始假设,因此本发明采用最大类间方差法(OTSU)来进行图像分割。OTSU算法通过计算最佳阈值,将原图像分割为前 景和背景两部分。假设阈值为T,则最佳阈值T*可以通过以下算法获得:3. Coastline extraction. The extraction schematic diagram is shown in Fig. 4, because the spectral responses of water and land are different in different bands, the histograms of the combined images of three different bands present bimodality, which meets the initial assumption of the OTSU algorithm, so the present invention adopts the maximum between-class variance method (OTSU ) for image segmentation. The OTSU algorithm divides the original image into two parts, the foreground and the background, by calculating the optimal threshold. Assuming the threshold is T, the optimal threshold T* can be obtained by the following algorithm:
W0=N0/(N0+N1)W 0 =N 0 /(N 0 +N 1 )
W1=N1/(N0+N1)W 1 =N 1 /(N 0 +N 1 )
W0+W1=1W 0 +W 1 =1
U=W0*U0+W1*U1 U=W 0 *U 0 +W 1 *U 1
G=W0*(U0-U)2+W1*(U1-U)2 G=W 0 *(U 0 -U) 2 +W 1 *(U 1 -U) 2
其中像元灰度值小于阈值T的像元个数为N0,像元灰度大于阈值T的像元个数为N1,W0为前景像元个数占图像比例,U0为前景平均灰度,W1为背景像元个数占图像比例,U1为 背景平均灰度,U为整图平均灰度,G为类间方差。Among them, the number of pixels whose gray value is less than the threshold T is N 0 , the number of pixels whose gray value is greater than the threshold T is N 1 , W 0 is the proportion of the number of foreground pixels in the image, and U 0 is the foreground The average gray level, W 1 is the proportion of the number of background pixels in the image, U 1 is the average gray level of the background, U is the average gray level of the whole image, and G is the variance between classes.
当类间方差G最大时,此时的阈值T即为最佳阈值T*。因为背景和前景之间的类间方 差越大,说明构成图像的两部分的差别越大,当部分前景错分为背景或部分背景错分为前景 都会导致两部分差别变小。因此,使类间方差最大的最佳阈值T*可以获得错分概率最小的 图像分割结果。When the inter-class variance G is the largest, the threshold T at this time is the optimal threshold T*. Because the greater the inter-class variance between the background and the foreground, the greater the difference between the two parts of the image. When part of the foreground is misclassified as the background or part of the background is misclassified as the foreground, the difference between the two parts will become smaller. Therefore, the optimal threshold T* that maximizes the variance between classes can obtain the image segmentation result with the smallest probability of misclassification.
然后,利用区域增长算法和形态膨胀滤波器对分割结果进行处理,将陆地和水域中的 “杂质”去掉,最终获得一系列表示初始海岸线位置的像元。Then, the segmentation result is processed by region growing algorithm and morphological expansion filter to remove the "impurity" in the land and water, and finally obtain a series of pixels representing the initial coastline position.
4.海岸线变化控制点提取。对初始海岸线进行海岸线变化控制点提取,获得初始海岸 线变化控制点,通过初始海岸线变化控制点将所述初始海岸线分段,获得若干段分段海岸 线。提取初始海岸线变化控制点是利用Harris角点检测算法,即计算3中初始海岸线上每 一个像元的Harris响应值R;经过阈值判定和邻域抑制的筛选,并结合初始海岸线的整体 形态选出R中的局部最大值,即为该初始海岸线上的变化控制点。由于计算Harris响应值 R时,初始海岸线中像元按连接关系依次计算,因此获取的变化控制点位置也按连接关系 依次存储。海岸线变化控制点提取示意图见图5。4. Coastline change control point extraction. The coastline change control points are extracted from the initial coastline to obtain the initial coastline change control points, and the initial coastline is segmented through the initial coastline change control points to obtain several segmented coastlines. The initial coastline change control point is extracted using the Harris corner detection algorithm, that is, the Harris response value R of each pixel on the initial coastline is calculated in 3; after threshold determination and neighborhood suppression screening, combined with the overall shape of the initial coastline to select The local maximum in R is the change control point on the initial coastline. When calculating the Harris response value R, the pixels in the initial coastline are calculated in sequence according to the connection relationship, so the obtained change control point positions are also stored in sequence according to the connection relationship. The schematic diagram of coastline change control point extraction is shown in Figure 5.
5.亚像元定位及偏差纠正。在4的基础上,对每一段分段海岸线内的初始海岸点(像 元级)进行亚像元定位。基于邻域像元灰度的亚像元定位算法是基于一个假设:根据邻近 原理,每个像素的灰度值由像素中不同物体的灰度值加权,并且相邻像素之间相同物体的 概率最大。在初步的海岸线像素周围只有两种地物—陆地和水体,所以我们可以认为每个 初步海岸线像素的灰度值是由相邻的纯像素加权的。因此,亚像元定位算法分为两个阶段 —确定邻域窗口大小和计算亚像元坐标位置。亚像元定位结果见图6。5. Sub-pixel positioning and deviation correction. On the basis of 4, perform sub-pixel positioning on the initial coast points (pixel level) in each segmented coastline. The sub-pixel positioning algorithm based on the neighborhood pixel gray is based on an assumption: according to the principle of proximity, the gray value of each pixel is weighted by the gray value of different objects in the pixel, and the probability of the same object between adjacent pixels maximum. There are only two kinds of features around the preliminary coastline pixels—land and water, so we can think that the gray value of each preliminary coastline pixel is weighted by adjacent pure pixels. Therefore, the sub-pixel localization algorithm is divided into two stages—determining the size of the neighborhood window and calculating the sub-pixel coordinate position. The results of sub-pixel positioning are shown in Figure 6.
窗口大小的选择需要满足一个前提条件,即在不违反邻近原理的基础上,保证拟合曲 线能从邻域窗口的一侧进入,从另一侧穿出。因此,在初始图像分割的水路二值图基础 上,计算每一个初始海岸线像元的边缘方向,根据不同的边缘方向给予初始海岸线像元不 同的邻域窗口。当边缘方向绝对值小于等于1时,使用5×3的邻域窗口;当边缘方向绝对值大于1时,使用3×5的邻域窗口。The choice of window size needs to meet a prerequisite, that is, on the basis of not violating the principle of proximity, it is guaranteed that the fitting curve can enter from one side of the neighborhood window and pass through from the other side. Therefore, on the basis of the waterway binary image segmented in the initial image, the edge direction of each initial coastline pixel is calculated, and different neighborhood windows are given to the initial coastline pixel according to different edge directions. When the absolute value of the edge direction is less than or equal to 1, a 5×3 neighborhood window is used; when the absolute value of the edge direction is greater than 1, a 3×5 neighborhood window is used.
在邻域窗口确认后,计算亚像元坐标位置。我们通过拟合函数,将领域窗口内的边界 描述为一个曲线,通过计算出拟合函数的参数,来确定亚像元定位点的坐标位置。基于2中计算Landsat 8融合影像与参考影像之间的几何位移(dx,dy),对亚像元海岸线定位 点的地理坐标进行误差纠正。After confirmation in the neighborhood window, calculate the sub-pixel coordinate position. We describe the boundary of the domain window as a curve through the fitting function, and determine the coordinate position of the sub-pixel anchor point by calculating the parameters of the fitting function. Based on the geometric displacement (dx, dy) between the Landsat 8 fusion image and the reference image calculated in 2, the geographic coordinates of the sub-pixel coastline positioning points are corrected for errors.
6.定位点拟合。由于大部分海岸线除角点(海岸线变化控制点)外基本上为一条平滑 的线段,因此可以对每一段分段海岸线内的亚像元级定位点进行最小二乘法的二次拟合, (即利用像元级尺度下的海岸线整体形态对亚像元级尺度下的定位结果进行纠正)。将5 中每一段分段海岸线内提取的亚像元定位点视为一个点集,因此我们可以认为同一个点集 内的亚像元定位点间曲率没有大的变化,然后对同一点集的中的亚像元定位点进行最小二 乘拟合,分段拟合示意图见图7。对相邻线段接点处设置连接性约束,并进行圆滑处理, 得到一条完整的连续的亚像元级海岸线,实现超分辨率制图。实验区1结果见图8,实验区2结果见图9。6. Anchor point fitting. Since most of the coastlines are basically a smooth line except for the corner points (coastline change control points), the quadratic fitting of the least squares method can be performed on the sub-pixel-level positioning points in each section of the coastline, (ie Use the overall shape of the coastline at the pixel level to correct the positioning results at the sub-pixel level). The sub-pixel anchor points extracted in each segmented coastline in 5 are regarded as a point set, so we can think that the curvature of the sub-pixel anchor points in the same point set does not have a big change, and then the same point set The least squares fitting is carried out on the sub-pixel positioning points in , and the schematic diagram of segment fitting is shown in Figure 7. Connectivity constraints are set at the joints of adjacent line segments and smoothed to obtain a complete and continuous sub-pixel-level coastline to achieve super-resolution mapping. The results of experimental area 1 are shown in Figure 8, and the results of experimental area 2 are shown in Figure 9.
7.误差分析。分别计算所述海岸线亚像元定位坐标与参考海岸线之间、海岸线矢量结 果与参考海岸线之间的位置误差并分析误差结果。误差分析结果见表2与表3所示。7. Error analysis. Calculate respectively the location error between the coastline sub-pixel positioning coordinates and the reference coastline, the coastline vector result and the reference coastline, and analyze the error results. The error analysis results are shown in Table 2 and Table 3.
表2 误差统计Table 2 Error statistics
表3 误差统计Table 3 Error statistics
表2总结了每个日期,每个实验区域和每个数据类型的误差统计(平均误差,标准偏 差)。平均误差是通过对所有误差进行平均而得到的,因为所有误差都是通过计算从最终海 岸线点到参考海岸线的距离的绝对值而获得的,所以使用平均误差来解释对参考海岸线的 偏差水平。标准偏差(STDEV)表示平均误差周围的变化性。Table 2 summarizes the error statistics (mean error, standard deviation) for each date, each experimental region, and each data type. The average error is obtained by averaging all the errors, and since all errors are obtained by calculating the absolute value of the distance from the final shoreline point to the reference shoreline, the average error is used to account for the level of deviation from the reference shoreline. The standard deviation (STDEV) represents the variability around the mean error.
从表3中可知,基于局部临域灰度的亚像元定位算法有效的提高了海岸线提取的精度, 其平均误差值在3.15和5.87之间,标准偏差在1.07至2.06,定位精度改善幅度在30%~ 60%之间。对比三种不同的波段组合(选择)的亚像元海岸线精度。显然,用MNDWI数据得 到的海岸线精度最高,其总的平均误差值最小,在不同的领域,大多数平均误差也最小。 这表明MNDWI是具体最佳抗噪性的水体指数。结合实验区1和实验区2的结果,本发明提出的基于半全局超分辨率制图方法具有很好的普适性,能适应不同曲率海岸线。It can be seen from Table 3 that the sub-pixel positioning algorithm based on the local neighborhood gray can effectively improve the accuracy of coastline extraction. Between 30% and 60%. Comparing sub-pixel coastline accuracies for three different band combinations (selections). Obviously, the coastline obtained with MNDWI data has the highest accuracy, and its total average error value is the smallest. In different fields, most of the average errors are also the smallest. This shows that MNDWI is specifically the water body index with the best noise immunity. Combining the results of Experimental Area 1 and Experimental Area 2, the semi-global super-resolution mapping method proposed by the present invention has good universality and can adapt to coastlines with different curvatures.
上面结合附图对本发明的实施例进行了描述,但是本发明并不局限于上述的具体实施 方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在 本发明的启示下,在不脱离本发明宗旨和权利要求所保护的范围情况下,还可做出若干改 进和变形,这些均属于本发明的保护之内。Embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementations, and the above-mentioned specific implementations are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, without departing from the gist of the present invention and the scope of protection of the claims, several improvements and modifications can also be made, and these all belong to the protection of the present invention.
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