等值线平滑的噪声地图矢量化优化方法

Vector Optimization Method of Noise Maps Based on Isopleth Smoothing

噪声地图中栅格型噪声云图被放大后会产生失真现象,且占用的存储空间较大,不利于大尺度噪声地图的精细化展示。为提升噪声云图展示效果并降低文件大小,研究双线性插值、双三次卷积插值、栅格图像矢量化方法的优缺点,提出一种基于噪声等值线平滑的矢量化优化方法。该方法利用平滑后的等值线、绘图边界、建筑物轮廓等构建矢量面要素,并根据各面要素内噪声值通过渲染得到矢量噪声云图。不同噪声云图优化方法的对比结果表明,根据基于等值线平滑的优化方法得到的矢量噪声云图对噪声传播和分布规律的展示更为清晰,数据量降为栅格数据的1/30,且噪声展示精度灵活可调,对于改善噪声地图质量、增强噪声地图实用性具有重要意义。该方法也可推广应用于绘制其他类似的专题地图以提升数据可视化效果。

The raster noise cloud image of noise maps will produce distortion when enlarged, and occupy much more storage space, which is not conducive to display the detail of large scale noise maps. To improve the image quality and reduce the file size, the advantages and disadvantages of bilinear interpolation, bi-cubic convolution interpolation and raster image vectorization methods are studied, and a new vector optimization method based on noise isopleth smoothing is proposed. The new method uses the smoothed noise isopleth, the drawing boundary and the building contours to construct the vector plane elements, and then dyes the noise cloud image according to the interior noise value of each vector plane element. The comparison of different noise map optimization methods show that, the vector noise cloud image obtained by the optimization method based on isopleth smoothing is more distinct than other methods in displaying the noise propagation and distribution while the file size is reduced to 1/30 of the original raster image, and the noise display precision is adjustable. The method is of great significance to improve the quality of noise cloud image and enhance the practicability of noise maps. This method can also be applied to other similar thematic maps to improve the quality of data visualization.