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Matching between Different Image Domains

机译:不同图像域之间的匹配

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Most of the image registration/matching methods are applicable to images acquired by either identical or similar sensors from various positions. Simpler techniques assume some object space relationship between sensor reference points, such as near parallel image planes, certain overlap and comparable radiometric characteristics. More robust methods allow for larger variations in image orientation and texture, such as the Scale-Invariant Feature Transformation (SIFT), a highly robust technique widely used in computer vision. The use of SIFT, however, is quite limited in mapping so far, mainly, because most of the imagery are acquired from airborne/spaceborne platforms, and, consequently, the image orientation is better known, presenting a less general case for matching. The motivation for this study is to look at the feasibility of a particular case of matching between different image domains. In this investigation, the co-registration of satellite imagery and LiDAR intensity data is addressed.
机译:大多数图像配准/匹配方法适用于由相同或相似传感器从各个位置获取的图像。更简单的技术假设传感器参考点之间存在某种对象空间关系,例如接近平行的图像平面,某些重叠和可比较的辐射特性。更加健壮的方法可以使图像方向和纹理发生更大的变化,例如比例不变特征变换(SIFT),一种在计算机视觉中广泛使用的高度健壮的技术。但是,到目前为止,SIFT的使用在制图方面受到很大限制,主要是因为大多数图像是从机载/星载平台获取的,因此,图像方向更好地为人所知,这为匹配提供了一种不太普遍的情况。这项研究的目的是要研究在不同图像域之间进行匹配的特定情况的可行性。在这项调查中,解决了卫星图像和LiDAR强度数据的共同注册问题。

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