基于正负核密度曲线的线要素局部清晰度估算与自适应分段

doi:10.11947/j.AGCS.2024.20230287

摘要/Abstract

摘要：

在线要素化简时，根据特征差异对线要素进行分段是合理运用化简方法的关键。然而现有分段方法侧重分析线要素的形态特征，忽略了线要素在不同比例尺表达的视觉差异。线要素中无法清晰辨识的模糊部位会随比例尺的变化而改变。基于此，本文提出了一种基于局部清晰度的线要素分段方法。首先，在特定比例尺下生成线要素的栅格形态，并将栅格线像素分为3类：单边界像素、双边界像素和内部像素，其中单边界像素和内部像素会严重影响线要素的视觉辨识；其次，建立3类像素与原矢量折点的映射关系，得到两组数据点：对应线要素模糊部位的粘连折点和对应线要素清晰部位的正常折点；然后，基于一维核密度进行两组数据点的聚集性分析，分别生成该尺度下表示线要素清晰度变化的正向核密度曲线和表示线要素模糊程度变化的负向核密度曲线；最后，分析正负核密度交点特征，得到划分线要素清晰与模糊区域的分段点并完成线要素分段。通过与人为分段结果对比可知，本文的分段结果与人眼对线要素模糊与清晰部分的识别基本一致。

关键词: 多尺度, 异质性线要素, 局部清晰度, 核密度, 线要素分段

Abstract:

In line simplification, segmentation of map line features according to differences in morphological features is the key to rational use of simplification methods. The existing segmentation methods are mainly based on vertices to analyze the morphological heterogeneity of line features and ignore the morphological changes of line features expressed at different scales. The fuzzy parts that cannot be clearly identified in the heterogeneous line features will change with the different scales. Based on this, a method for segmenting line features based on clarity changes is proposed in this paper. Firstly, the raster pattern of line features is generated at a specific scale, and the raster line pixels are classified into three types of pixels: single-boundary pixels, double-boundary pixels, and internal pixels; single-boundary pixels and internal pixels, which affect visual discrimination; the mapping relationship between the three types of pixels and the original vector line is established, and two groups of data points are obtained: adhesive vertices, which correspond to the blurred parts of the line features, and normal vertices, which correspond to the clear parts of the line features; and the aggregation analysis of the two groups of data points based on the kernel density, and the clustering analysis is generated, and generate the positive kernel density curve which indicates the change of line feature clarity and the negative kernel density curve which indicates the change of line feature blurring degree under this scale; finally, analyze the characteristics of the intersection of two kernel density curves to get the segmentation point which divides the clarity and blurred parts of the line feature and complete the segmentation of the line. By comparing the segmentation results with manually segmented results, it is evident that the segmentation results of this paper are generally consistent with the human eye's identification of fuzzy and blurred parts of the line features.

Key words: multiscale, heterogeneous line features, local clarify, kernel density estimation, line features segmentation

中图分类号:

P208

成晓强, 刘娜. 基于正负核密度曲线的线要素局部清晰度估算与自适应分段[J]. 测绘学报, 2024, 53(6): 1180-1194.

Xiaoqiang CHENG, Na LIU. Local clarity estimation and adaptive segmentation of line features based on positive and negative kernel density curves[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1180-1194.

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类别	经纬度范围	长度/m	地区特征和数据特点
道路	28.08°N，103.57°E	18 877.36	该地区山高坡陡，峡长谷深，地形地貌复杂；道路数据为盘山公路，局部蜿蜒曲折，局部平坦开阔，具备明显异质性
	—
	28.03°N，103.63°E
道路	27.73°N，103.88°E	30 271.45
	—
	27.68°N，103.93°E
海岸线	29.22°N，95.00°W	73 801.66	该地区海岸线受到人为活动影响，局部形态规则且密集，局部自然弯曲，具备显著异质性
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	29.18°N，94.93°W
海岸线	35.47°N，76.69°W	128 032.76	该沿海地区海岸线形态受人为影响较小，自然形成的局部细小分枝状弯曲和局部较大弯曲，数据具备明显异质性
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	35.38°N，76.57°W
海岸线	37.66°N，75.89°W	51 909.13	该沿海地区海岸线形态受人为影响较小，自然形成局部密集细小弯曲和局部较大弯曲，数据具备明显异质性
	—
	37.63°N，75.85°W