摘要：
<jats:p> Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches. </jats:p>
期刊：
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives,2018年42(3):2315-2320 ISSN：1682-1750
作者机构：
[Guoxin Tan; Wenyuan Zhang; Xiaohan Kong; Songyin Zheng] National Research Center of Cultural Industries,Central China Normal University
会议名称：
ISPRS TC III Mid-term Symposium:Developments ,Technologies and Applications in Remote Sensing (国际摄影测量与遥感学会“遥感:技术、发展、应用”国际学术会议)
会议时间：
2018-05-07
会议地点：
北京
会议论文集名称：
ISPRS TC III Mid-term Symposium:Developments ,Technologies and Applications in Remote Sensing (国际摄影测量与遥感学会“遥感:技术、发展、应用”国际学术会议) 论文集
关键词：
Change Detection;Urban Lakes;Multi-temporal Remote Sensing Images;Modified NDWI;Wuhan City
摘要：
<jats:p>Abstract. Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37 km2 between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.
</jats:p>
作者机构：
[Tan, Guoxin; Zhang, Wenyuan; Zheng, Songyin; Zhang, WY; Liu, Zhaobin; Kong, Xiaohan; Sun, Chuanming] Cent China Normal Univ, Natl Res Ctr Cultural Ind, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhang, WY; Tan, GX] C;Cent China Normal Univ, Natl Res Ctr Cultural Ind, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
CART;Change detection;Land cover;Multi-temporal image;Shahu lake
摘要：
The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km<sup>2</sup>to ~3.60 km<sup>2</sup>during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake's shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.<br/> 2018 by the authors. Licensee MDPI, Basel, Switzerland.
