Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2002, Vol. 14 Issue (1) : 61-64     DOI: 10.6046/gtzyyg.2002.01.14
GIS |
THE DESIGNING OF INTELLECTUALIZED POLICED GIS SOFTWARE
DONG Zhi-ying, LI Bing
College of Environment and Resources of Jilin University, Changchun 130026, China
Download: PDF(315 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

According to the consistency of time and space in the information of policed GIS, this paper introduces the time-vector and the technology of "data-digging", and designs the system of intellectualized policed GIS software. The system inosculates the analysis of time-space with logistic consequence, and has the functions of realizing fast prediction and assisting policy decision.

Keywords Texture      LBP      Classification     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
SONG Ben-Qin
LI Pei-Jun
Cite this article:   
SONG Ben-Qin,LI Pei-Jun. THE DESIGNING OF INTELLECTUALIZED POLICED GIS SOFTWARE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(1): 61-64.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.01.14     OR     https://www.gtzyyg.com/EN/Y2002/V14/I1/61


[1] Jack Dangermond, The future of GIS Technology
[A].Proceedings o f The 20th Asian Conference on Remote Sensing
[C].1999.


[2] 薛华成.管理信息系统(第三版)[M].北京:清华大学出版社,1999.


[3] 邝孔武,王晓敏.信息系统分析与设计[M].北京:清华大学出版社,1999.


[4] 陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社,1999.

[1] ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[2] SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[3] WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[4] LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[5] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[6] FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[7] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[8] WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[9] JIANG Xiao, ZHONG Chang, LIAN Zheng, WU Liangting, SHAO Zhitao. Research progress on classification criterion of geological information products based on satellite remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 279-283.
[10] BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[11] HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[12] LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[13] MENG Qing, BAI Hongying, ZHAO Ting, GUO Shaozhuang, QI Guizeng. The eco-barrier effect of Qinling Mountain on aerosols[J]. Remote Sensing for Land & Resources, 2021, 33(1): 240-248.
[14] XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[15] JIANG Shan, WANG Chun, SONG Hongli, LIU Yufeng. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech