Spatial distribution characteristics of urban landscape pattern based on multi-source remote sensing technology

被引:0
|
作者
Liu, Sai [1 ]
机构
[1] Hunan Inst Technol, Hengyang 421000, Hunan, Peoples R China
关键词
multi-source remote sensing technology; urban landscape pattern space; distribution feature extraction; secondary grid division;
D O I
10.1504/IJETM.2021.115727
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In order to overcome the low efficiency of feature extraction in traditional research methods for spatial distribution of urban landscape pattern, a new research method based on multi-source remote sensing technology is proposed. Combined with the idea of information entropy and grid division, the spatial secondary grid division of urban landscape pattern is completed by multi-source remote sensing technology. The probability density of spatial distribution of urban landscape pattern is calculated according to the results of secondary grid division. The scale pyramid is established to study the spatial distribution characteristics of urban landscape pattern. The experimental results show that the proposed method can effectively realise the research of spatial distribution characteristics of urban landscape pattern, with high efficiency of feature extraction, and the maximum extraction time is only 0.22 min.
引用
收藏
页码:33 / 48
页数:16
相关论文
共 50 条
  • [31] A Study on Urban Thermal Field of Shanghai Using Multi-source Remote Sensing Data
    Li, Cheng-Fan
    Yin, Jing-Yuan
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2013, 41 (04) : 1009 - 1019
  • [32] Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data
    Wang, Xinchuang
    Jiao, Haiming
    IEEE ACCESS, 2020, 8 : 178870 - 178885
  • [33] A Study on Urban Thermal Field of Shanghai Using Multi-source Remote Sensing Data
    Cheng-Fan Li
    Jing-Yuan Yin
    Journal of the Indian Society of Remote Sensing, 2013, 41 : 1009 - 1019
  • [34] Mapping urban land use by combining multi-source social sensing data and remote sensing images
    Wenliang Li
    Earth Science Informatics, 2021, 14 : 1537 - 1545
  • [35] Mapping urban land use by combining multi-source social sensing data and remote sensing images
    Li, Wenliang
    EARTH SCIENCE INFORMATICS, 2021, 14 (03) : 1537 - 1545
  • [36] Mallat fusion for multi-source remote sensing classification
    Cao, Dongdong
    Yin, Qian
    Guo, Ping
    ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, 2006, : 588 - 593
  • [37] Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery
    Song, Shiran
    Liu, Jianhua
    Liu, Yuan
    Feng, Guoqiang
    Han, Hui
    Yao, Yuan
    Du, Mingyi
    SENSORS, 2020, 20 (02)
  • [38] High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion
    Pi X.
    Zeng Y.
    He C.
    National Remote Sensing Bulletin, 2021, 25 (06) : 1216 - 1226
  • [39] Data fusion of multi-source remote sensing based on level set method and application to urban road extraction
    Key Laboratory for Wave Scattering and Remote Sensing Information, Fudan University, Shanghai 200433, China
    Dianzi Yu Xinxi Xuebao, 2007, 6 (1464-1470):
  • [40] ASSESSING TEMPORAL AND SPATIAL VARIATIONS OF VEGETATION DEGRADATION IN SOUTHWEST CHINA BASED ON MULTI-SOURCE REMOTE SENSING DATA
    Xu, Yali
    Zhang, Mingfang
    Yu, Enxu
    Hou, Yiping
    Yang, Chen
    Deng, Shiyu
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6005 - 6008