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Multi-scale Automatic Extraction of Terrain Structure Lines Based on Wavelet Analysis

机译:基于小波分析的地形结构线多尺度自动提取

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Terrain structure lines (valleys and ridges) are very important for geomorphological characterization, they construct the skeleton of terrain undation and variation, and they are scale-dependent because the ones are more important at a larger geographical scale than their counterparts at a smaller. Wavelet analysis is a new branch of mathematics burgeoning at the end of 80s. It has double meanings simultaneously on profundity of theory and extent of application. Because it has good local character at both time or space and frequency field simultaneously, and sample interval of signal can be adjusted automatically with different frequency components, wavelet analysis method can adapt to different type of relief at the same time, such as mountainous or plain terrain or the both. This paper formulates briefly the basic principle of multiresolution analysis(MRA) on wavelet transform, represents a new method of multi-scale automatic extraction of terrain structure lines from contour data. It employs curvature to describe contour data through curve spline fitting, then decomposes curvature based on the wavelet MRA at multiple scales, thereafter determines the contributive size of curvature by inspecting the wavelet coefficients at different scale and records their corresponding positions exactly at which describes the degree of convexity and concavity. Accordingly, we can get the candidate point sets belong to the structure lines at different scale. Assuming the higher elevation is to the left and the lower is to the right along a contour line, the positive curvature represents the convexity, namely ridges, the negative represents the concavity, namely valleys. Therefore, the point sets belong to valleys or ridges can be sorted out easily from the candidate point sets at different scale. According to the basic rules of relief contour representation, multi-scale valleys and ridges can be determined from the point sets belong to valleys or ridges by identifying ascription of bisector at concave comer or convex corner on adjacent contours. Some practical examples are given to exam the method. Finally, this paper also discusses the efficiency and accuracy of the algorithms, and simply compares it with existing method.
机译:地形结构线(山谷和脊)对于地貌特征非常重要,它们构建了地形起伏和变化的骨架,它们是尺度依赖性的,因为这些尺度依赖性,因为那些在更大的地理标度比其对应物更重要,而不是较小的。小波分析是80年代末的数学蓬勃发展的新分支。它具有与理论和应用程度的重大相同的双重含义。因为它在两次或空间和频率场同时具有良好的本地字符,并且可以使用不同的频率分量自动调节信号的采样间隔,小波分析方法同时适应不同类型的浮雕,例如山区或平原地形或两者。本文简要制定了多辨别分析(MRA)对小波变换的基本原理,表示来自轮廓数据的多尺度自动提取地形结构线的新方法。它采用曲率来描述通过曲线花键拟合来描述轮廓数据,然后通过在多个尺度处基于小波MRA分解曲率,此后通过在不同刻度处检查小波系数并准确地记录其描述程度的对应位置来确定曲率的贡献大小。凸性和凹陷。因此,我们可以获得候选点集属于不同规模的结构线。假设较高的高度是左侧,下部沿着轮廓线向右,正曲率表示凸起,即脊,负代表凹陷,即谷。因此,点集属于谷或脊可以容易地从不同刻度设置候选点集。根据释放轮廓表示的基本规则,通过识别在相邻轮廓上的凹面或凸角处的分料归档,可以从点集合到谷或脊的点集合的多尺度谷谷和脊。一些实际的例子是给出方法。最后,本文还讨论了算法的效率和准确性,并简单地将其与现有方法进行比较。

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