Self-adaptive IDW interpolation method
An interpolation method and interpolation technology, applied in the fields of cartography and geographic information science, to achieve the effects of advanced methods, improved interpolation accuracy, and error avoidance
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2018-09-28
- Estimated Expiration
- Not applicable · inactive patent
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Abstract
Description
technical field
[0001] The invention belongs to the field of cartography and geographic information science and technology, and relates to an IDW interpolation method that takes into account the actual distribution density of spatial point data. Background technique
[0002] Spatial interpolation is to establish a certain interpolation function relationship F(x) through limited discrete sampling points, and substitute any position within the range of known sampling points into the function relationship to calculate the attribute value of the point. At present, common spatial interpolation methods include kriging interpolation, natural neighbor interpolation, spline function interpolation, multiple regression method, trend surface method, inverse distance weight (IDW) interpolation, etc. Among them, IDW interpolation is a common and convenient spatial interpolation method. It has a wide range of applications in DEM digital elevation model construction, meteorological element ...
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Embodiment Construction
[0017] In order to describe the technical content, structural features, objectives and effects of the present invention in detail, the following will be described in detail in conjunction with specific embodiments.
[0018] The implementation steps of the present invention can be summarized into two parts: data preprocessing and high-density saliency analysis within the second-order radius and first-order radius of the space point to be interpolated. Each implementation step is further described below.
[0019] The steps of data preprocessing include:
[0020] Step 1: Data cleaning. Due to the actual distribution of spatial data points, there may be cases of overlapping each other, that is, there are two or more points in one position, which will affect the subsequent generation of Delaunay triangulation. Therefore, keep any one of the spatial points located at the same position and with capping;
[0021] Step 2: Generate a Delaunay triangulation from the cleaned spatial po...