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首页> 外文会议>Conference on Image and Signal Processing for Remote Sensing VIII, Sep 24-27, 2002, Agia Pelagia, Crete, Greece >Extraction of Object Skeletons in Multi-Spectral Imagery by the Orthogonal Regression Fitting
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Extraction of Object Skeletons in Multi-Spectral Imagery by the Orthogonal Regression Fitting

机译：正交回归拟合提取多光谱图像中的目标骨架

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摘要

Accurate and automatic extraction of skeletal shape of objects of interest from satellite images provides an efficient solution to such image analysis tasks as object detection, object identification, and shape description. The problem of skeletal shape extraction can be effectively solved in three basic steps: intensity clustering (i.e. segmentation) of objects, extraction of a structural graph of the object shape, and refinement of structural graph by the orthogonal regression fitting. The objects of interest are segmented from the background by a clustering transformation of primary features (spectral components) with respect to each pixel. The structural graph is composed of connected skeleton vertices and represents the topology of the skeleton. In the general case, it is a quite rough piecewise-linear representation of object skeletons. The positions of skeleton vertices on the image plane are adjusted by means of the orthogonal regression fitting. It consists of changing positions of existing vertices according to the minimum of the mean orthogonal distances and, eventually, adding new vertices in-between if a given accuracy if not yet satisfied. Vertices of initial piecewise-linear skeletons are extracted by using a multi-scale image relevance function. The relevance function is an image local operator that has local maximums at the centers of the objects of interest. The main application area of this algorithm is the extraction and tracing of hydrographic objects such as rivers.
机译：从卫星图像中准确自动提取感兴趣对象的骨骼形状，为诸如对象检测，对象识别和形状描述之类的图像分析任务提供了有效的解决方案。骨骼形状提取的问题可以通过三个基本步骤有效地解决：对象的强度聚类（即分割），对象形状的结构图的提取以及通过正交回归拟合对结构图的细化。通过针对每个像素的主要特征（光谱分量）的聚类转换，从背景中分割出感兴趣的对象。结构图由相连的骨架顶点组成，代表骨架的拓扑。在一般情况下，它是对象骨架的相当粗略的分段线性表示。借助正交回归拟合来调整图像顶点上骨架顶点的位置。它包括根据平均正交距离的最小值更改现有顶点的位置，如果尚未满足给定的精度，则最终在它们之间添加新的顶点。通过使用多尺度图像相关函数提取初始分段线性骨架的顶点。相关函数是图像局部算子，在感兴趣对象的中心具有局部最大值。该算法的主要应用领域是河流等水文目标的提取和跟踪。

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