太阳成8722(中国)有限公司-GREEN NO.1

标题：An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis

作者：Liu, YL (Liu, Yaolin); Wang, XM (Wang, Xiaomi); Liu, QL (Liu, Qiliang); Chen, YY (Chen, Yiyun); Liu, LL (Liu, Leilei)

来源出版物：ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION卷：6期：5 文献编号：118 DOI：来源出版物：ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION卷：6期：5 文献编号：127 出版年：APR 2017

摘要：Time series clustering algorithms have been widely used to mine the clustering distribution characteristics of real phenomena. However, these algorithms have several limitations. First, they depend heavily on prior knowledge. Second, the algorithms do not simultaneously consider the similarity of spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends ( trends in terms of the change direction and ranges in addition and deletion over time), which are all important similarity measurements. Finally, the calculation cost based on these methods for clustering analysis is becoming increasingly computationally demanding, because the data volume of the image time series data is increasing. In view of these shortcomings, an improved density-based time series clustering method based on image resampling ( DBTSC-IR) has been proposed in this paper. The proposed DBTSC-IR has two major parts. In the first part, an optimal resampling scale of the image time series data is first determined to reduce the data volume by using a new scale optimization function. In the second part, the traditional density-based time series clustering algorithm is improved by introducing a density indicator to control the clustering sequences by considering the spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends. The final clustering analysis is then performed directly on the resampled image time series data by using the improved algorithm. Finally, the effectiveness of the proposed DBTSC-IR is illustrated by experiments on both the simulated datasets and in real applications. The proposed method can effectively and adaptively recognize the spatial patterns with arbitrary shapes of image time series data with consideration of the effects of noise.

入藏号：WOS:000404525000027

文献类型：Article

语种：English

作者关键词： time series clustering; time series resampling; density-based clustering; spatial data mining; surface deformation patterns

扩展关键词： SIMILARITY; ALGORITHM

通讯作者地址：Wang, XM (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

Liu, LL (reprint author), Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong 999077, Hong Kong, Peoples R China.

电子邮件地址：yaolinliuwhu@163.com; xiaomiw@yeah.net; qiliang.liu@csu.edu.cn; chenyy@whu.edu.cn; csulll@foxmail.com

地址：

[Liu, Yaolin; Wang, Xiaomi; Chen, Yiyun] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Liu, Yaolin] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Liu, Yaolin] Wuhan Univ, Collaborat Innovat Ctr Geospatial Informat Techno, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Liu, Qiliang] Cent S Univ, Dept Geoinformat, Changsha 410012, Hunan, Peoples R China.

[Chen, Yiyun] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Peoples R China.

[Chen, Yiyun] Wuhan Univ, Suzhou Inst, Suzhou 215123, Jiangsu, Peoples R China.

[Liu, Leilei] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong 999077, Hong Kong, Peoples R China.

研究方向：Physical Geography; Remote Sensing

ISSN：2220-9964

影响因子：0.371