Local clarity estimation and adaptive segmentation of line features based on positive and negative kernel density curves

doi:10.11947/j.AGCS.2024.20230287

Abstract

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

CLC Number:

P208

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.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Tab.1

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

References 28

[1]	翟仁健, 武芳, 朱丽, 等. 曲线形态的结构化表达[J]. 测绘学报, 2009, 38(2):175-182.
	ZHAI Renjian, WU Fang, ZHU Li, et al. Structured representation of curve shape[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(2):175-182.
[2]	朱鲲鹏, 武芳, 陈波, 等. 基于约束条件的线要素化简算法质量评估[J]. 测绘科学, 2007, 32(3):28-30, 192.
	ZHU Kunpeng, WU Fang, CHEN Bo, et al. Quality assessment of linear features' simplification algorithms based on constraint conditions[J]. Science of Surveying and Mapping, 2007, 32(3):28-30, 192.
[3]	武芳, 巩现勇, 杜佳威. 地图制图综合回顾与前望[J]. 测绘学报, 2017, 46(10):1645-1664.DOI:10.11947/j.AGCS.2017.20170287.
	WU Fang, GONG Xianyong, DU Jiawei. Overview of the research progress in automated map generalization[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1645-1664.DOI:10.11947/j.AGCS.2017.20170287.
[4]	SKOPELITI A, TSOULOS L. On the parametric description of the shape of the cartographic line[J]. Cartographica: the International Journal for Geographic Information and Geovisualization, 1999, 36(3):53-65.
[5]	STANĚK K, ŠILHÁK P, RYGLOVÁ A. A graphical generalization of localized morphological discontinuities on medium-scale state topographic maps[J]. Journal of Geovisualization and Spatial Analysis, 2022, 6(2):20.
[6]	魏泓丞, 张立华, 唐露露, 等. 顾及局部差异的航海图线要素复杂度度量[J]. 测绘学报, 2022, 51(9):1959-1968.DOI:10.11947/j.AGCS.2022.20210129.
	WEI Hongcheng, ZHANG Lihua, TANG Lulu, et al. Complexity measurement of line element on nautical chart considering local difference[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9):1959-1968.DOI:10.11947/j.AGCS.2022.20210129.
[7]	杨敏, 陈果, 李连营, 等. 序列卷积神经网络支持下线状地图目标的分段方法[J]. 测绘学报, 2023, 52(1):108-116.DOI:10.11947/j.AGCS.2023.20210317.
	YANG Min, CHEN Guo, LI Lianying, et al. Segmentation of linear map objects using sequential convolutional neural network[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1):108-116.DOI:10.11947/j.AGCS.2023.20210317.
[8]	肖天元, 刘鹏程, 艾廷华, 等. 一种傅里叶信息度量的曲线分形描述与多尺度表达方法[J]. 武汉大学学报(信息科学版), 2020, 45(1):119-125.
	XIAO Tianyuan, LIU Pengcheng, AI Tinghua, et al. A fractal description and multi-scale expression method of Fourier information metrics[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1):119-125.
[9]	刘鹏程, 肖天元, 肖佳, 等. 曲线多尺度表达的Head-Tail信息量分割法[J]. 测绘学报, 2020, 49(7):921-933.DOI:10.11947/j.AGCS.2020.20200004.
	LIU Pengcheng, XIAO Tianyuan, XIAO Jia, et al. A Head-Tail information break method oriented to multi-scale representation of polyline[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7):921-933.DOI:10.11947/j.AGCS.2020.20200004.
[10]	刘慧敏, 樊子德, 邓敏, 等. 地图上等高线信息度量的层次方法研究[J]. 测绘学报, 2012, 41(5):777-783.
	LIU Huimin, FAN Zide, DENG Min, et al. A hierarchical approach to measuring the information content of the contours in a map[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(5):777-783.
[11]	季宏超, 董箭, 李树军, 等. 顾及形态差异的TIN-DDM地形复杂度度量方法[J]. 测绘学报, 2023, 52(6):1010-1021.DOI:10.11947/j.AGCS.2023.20220074.
	JI Hongchao, DONG Jian, LI Shujun, et al. TIN-DDM terrain complexity measurement method considering topographic forms differences[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(6):1010-1021.DOI:10.11947/j.AGCS.2023.20220074.
[12]	程绵绵, 孙群, 徐立, 等. 面轮廓线相似性和复杂性度量及在化简中的应用[J]. 测绘学报, 2019, 48(4):489-501.DOI:10.11947/j.AGCS.2019.20180124.
	CHENG Mianmian, SUN Qun, XU Li, et al. Polygon contour similarity and complexity measurement and application in simplification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):489-501.DOI:10.11947/j.AGCS.2019.20180124.
[13]	LIU Minshi, HE Guifang, LONG Yi. A semantics-based trajectory segmentation simplification method[J]. Journal of Geovisualization and Spatial Analysis, 2021, 5(2):19.
[14]	BALBOA J L G, LÓPEZ F J A. Sinuosity pattern recognition of road features for segmentation purposes in cartographic generalization[J]. Pattern Recognition, 2009, 42(9):2150-2159.
[15]	刘鹏程, 杨琴. 贝叶斯模型下面向地图表达的海岸线分段模型[J]. 计算机工程与应用, 2016, 52(22):174-179.
	LIU Pengcheng, YANG Qin. Coastline segment model research for map generalization based on Bayesian method[J]. Computer Engineering and Applications, 2016, 52(22):174-179.
[16]	DU Jiawei, WU Fang, XING Ruixing, et al. Segmentation and sampling method for complex polyline generalization based on a generative adversarial network[J]. Geocarto International, 2022, 37(14):4158-4180.
[17]	MITROPOULOS V, NAKOS B. A methodology on natural occurring lines segmentation and generalization[C]//Proceedings of the 14th ICA Workshop on Generalisation and Multiple Representation. Paris: ICA, 2011.
[18]	陈军, 刘万增, 武昊, 等. 智能化测绘的基本问题与发展方向[J]. 测绘学报, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235.
	CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping: fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235.
[19]	郝彤, 王晓峰, 冯甜甜, 等. 地球系统多尺度关键区域与关键过程的智能化测绘[J]. 测绘学报, 2021, 50(8):1084-1095.DOI:10.11947/j.AGCS.2021.20210109.
	HAO Tong, WANG Xiaofeng, FENG Tiantian, et al. Intelligent and multi-scale surveying of key areas and processes of the Earth system[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1084-1095.DOI:10.11947/j.AGCS.2021.20210109.
[20]	陈军, 艾廷华, 闫利, 等. 智能化测绘的混合计算范式与方法研究[J/OL]. 测绘学报: 1-19 [2024-05-25].http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html.
	CHEN Jun, AI Tinghua, YAN Li, et al. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J/OL]. Acta Geodaetica et Cartographica Sinica: 1-19 [2024-05-25]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html.
[21]	安晓亚, 成晓强. 矢量曲线的视觉清晰度及在网络地图综合中的应用[J]. 测绘学报, 2020, 49(2):245-255.DOI:10.11947/j.AGCS.2020.20190280.
	AN Xiaoya, CHENG Xiaoqiang. Visual clarity of vector curve and its application in web map generalization[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2):245-255. DOI:10.11947/j.AGCS.2020.20190280.
[22]	SHEA K, MASTER R M. Cartographic generalization in a digital environment: when and how to generalize[C]//Proceedings of 1989 Auto-Carto 9. [S.l.]: IEEE, 1989.
[23]	唐炉亮, 阚子涵, 刘汇慧, 等. 网络空间中线要素的核密度估计方法[J]. 测绘学报, 2017, 46(1):107-113.DOI:10.11947/j.AGCS.2017.20150158.
	TANG Luliang, KAN Zihan, LIU Huihui, et al. A kernel density estimation method for linear features in network space[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1):107-113.DOI:10.11947/j.AGCS.2017.20150158.
[24]	禹文豪, 艾廷华. 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报, 2015, 44(1):82-90.DOI:10.11947/j.AGCS.2015.20130538.
	YU Wenhao, AI Tinghua. The visualization and analysis of POI features under network space supported by kernel density estimation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90.DOI:10.11947/j.AGCS.2015.20130538.
[25]	ANDERSON T K. Kernel density estimation and K-means clustering to profile road accident hotspots[J]. Accident Analysis & Prevention, 2009, 41(3):359-364.
[26]	BÍL M, ANDRÁŠIK R, JANOŠKA Z. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation[J]. Accident Analysis & Prevention, 2013, 55:265-273.
[27]	FLAHAUT B, MOUCHART M, SAN MARTIN E, et al. The local spatial autocorrelation and the kernel method for identifying black zones: a comparative approach[J]. Accident Analysis & Prevention, 2003, 35(6):991-1004.
[28]	WANG Zuyuan, GINZLER C, WASER L T. Assessing structural changes at the forest edge using kernel density estimation[J]. Forest Ecology and Management, 2020, 456:117639.

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
	—
	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
	—
	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