首页> 外文会议>International archives of the photogrammetry, remote sensing and spatial information sciences conference >MATCHING CONJUGATE POINTS BETWEEN MULTI RESOLUTION SATELLITE IMAGES USING GEOMETRIC AND RADIOMETRIC PROPERTIES
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MATCHING CONJUGATE POINTS BETWEEN MULTI RESOLUTION SATELLITE IMAGES USING GEOMETRIC AND RADIOMETRIC PROPERTIES

机译:使用几何和辐射性特性匹配多分辨率卫星图像之间的共轭点

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Remotely sensed images are the main source for a variety of mapping and change detection applications. Images from different satellites are employed in several of these applications. However, each type of these images has different resolution and orientation. Hence, they need to be co-registered before any meaningful utilization. The first step in the registration process is to find conjugate points between the images. This paper presents a modified approach of Scott and Longuet-Higgins approach to find conjugate points between different remotely sensed images. In such an algorithm, initially, comer points are extracted automatically in two images, and for each pair of points a cost value is computed. The cost of corresponding any two points is computed using image coordinates and pixel intensities. The cost values are then used to fill a cost matrix, and its SVD is used to find correspondent points. The algorithm is tested on three pairs of satellite images with different resolutions and orientations. Result shows that the presented approach succeeded in finding more than 96% of conjugate points between two different satellite images using only the image coordinates. Moreover, result shows that including the image intensities in the matching procedure does not improve the results significantly.
机译:远程感测图像是各种映射和改变检测应用的主要来源。来自不同卫星的图像在这些应用中使用。然而,每种类型的这些图像具有不同的分辨率和方向。因此,在任何有意义的利用之前,他们需要共同登记。注册过程中的第一步是在图像之间找到共轭点。本文介绍了Scott和Longuet-Higgins方法的修改方法,以找到不同远程感测图像之间的共轭点。在这样的算法中,最初,在两个图像中自动提取CLOP点,并且对于每对点计算成本值。使用图像坐标和像素强度来计算相应的任何两点的成本。然后使用成本值来填充成本矩阵,并且其SVD用于查找对应点。该算法在具有不同分辨率和方向的三对卫星图像上进行测试。结果表明,由于图像坐标,所呈现的方法成功地找到了两个不同卫星图像之间的超过96%的共轭点。此外,结果表明,包括匹配过程中的图像强度不会显着改善结果。

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