Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2013, Vol. 25 Issue (4) : 98-103     DOI: 10.6046/gtzyyg.2013.04.16
Technology and methodlogy |
Block feature usage in new construction land change detection supplemented by land use data
ZHANG Xi1, LIU Shunxi2, CHEN Ge1, WANG Zhongwu2, YOU Shucheng2
1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;
2. China Land Surveying and Planning Institute, Beijing 100035, China
Download: PDF(1525 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Aimed at solving the problems existent in the change detection of new construction land based on high-resolution optical images and land use data, this paper summed up the workflow of new construction land change detection supplemented by land use, analyzed the potential utility of spectra vegetation index, texture, gradient, and variance for land block in detecting new construction land, followed by a comparison of feature uses of unsupervised detection. Tests were carried out on 2 SPOT5 imageries covering rural-urban fringe areas located in Changping District of Beijing, with the utilization of 4 groups of features, which were spectra and vegetation index, spectra, vegetation index and texture, spectra, vegetation index and gradient, and spectra, vegetation index and variance. The results show that the performance using spectra and vegetation index feature is the best, and the addition of the other features would reduce the detection accuracy. Therefore, in order to achieve a good result in practical application, we should mainly use the result of spectra and vegetation index feature, aided by other results.
Keywords red soil      RS      soil erosion      soil and water conservation     
:  TP75.1  
Issue Date: 21 October 2013
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
HU Wenmin
ZHOU Weijun
YU Yuhang
BAO Chunhong
XIE Hongxia
Cite this article:   
HU Wenmin,ZHOU Weijun,YU Yuhang, et al. Block feature usage in new construction land change detection supplemented by land use data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 98-103.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.04.16     OR     https://www.gtzyyg.com/EN/Y2013/V25/I4/98
[1] 柴渊,李万东.土地利用动态遥感监测技术与方法[M].北京:地质出版社,2011. Chai Y,Li W D.The Techniques and methods of land-use dynamic remote sensing monitoring[M].Beijing:Geological Publishing House,2011.
[2] 李德仁.利用遥感影像进行变化检测[J].武汉大学学报:信息科学版,2003,28(s1):7-12. Li D R.Change detection from remote sensing images[J].Geomantics and Information Science of Wuhan University,2003,28(s1):7-12.
[3] Kennedy R E,Townsend P A,Gross J E.Remote sensing change detection tools for natural resource managers:Understanding concepts and tradeoffs in the design of landscape monitoring project [J].Remote Sensing of Environment,2009,113(7):1382-1396.
[4] 王琰,舒宁,龚颵.高分辨率遥感影像土地利用变化检测方法研究[J].国土资源遥感,2012,24(1):43-47. Wang Y,Shu N,Gong Y.A study of land use change detection based on high resolution remote sensing images[J].Remote Sensing for Land and Resources,2012,24(1):43-47.
[5] 叶明,杨晓平,蒋刚毅.基于TM遥感图像的宁波土地利用动态监测[J].宁波大学学报:理工版,2003,3(1):30-34. Ye M,Yang X P,Jiang G Y.Land use dynamic surveying based on the interpretation of TM image[J].Journal of Ningbo University (National Science and Engineering Edition),2003,3(1):30-34.
[6] 李磊,李小娟,崔伟宏.基于GIS和RS的县级土地利用动态监测系统研究[J].地理学与国土研究,2001,5(2):28-32. Li L,Li X J,Cui W H.On dynamic monitoring of land use at county level based on RS and GIS[J].Geography and Territorial Research,2001,5(2):28-32.
[7] Wu B,Yang J,Meng Y,et al.Fast detection of changed blocks in land use map[A].IGARSS 2012:6188-6191.
[8] 刘臻,宫鹏,史培军,等.基于相似度验证的自动变化探测研究[J].遥感学报,2005,9(5):537-543. Liu Z,Gong P,Shi P J,et al.Study on change detection automatically based on similarity calibration[J].Journal of Remote Sensing,2005,9(5):537-543.
[9] 袁修孝,宋妍.一种运用纹理和光谱特征消除投影差影响的建筑物变化检测方法[J].武汉大学学报:信息科学版,2007,32(6):489-493. Yuan X X,Song Y.A building change detection method considering projection influence based on spectral feature and texture feature[J].Geomatics and Information Science of Wuhan University,2007,32(6):489-493.
[10] Story M,Congalton R G.Accuracy assessment:A user's perspective[J].Photogrammetric Engineering and Remote Sensing,1986,53(3):397-399.
[1] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[2] QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[3] 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.
[4] REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[5] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[6] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[7] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[8] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[9] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[10] CHEN Jie, ZHANG Lifu, ZHANG Linshan, ZHANG Hongming, TONG Qingxi. Research progress on online monitoring technologies of water quality parameters based on ultraviolet-visible spectra[J]. Remote Sensing for Natural Resources, 2021, 33(4): 1-9.
[11] 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.
[12] GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[13] JIN Chengming, YANG Xingwang, JING Haitao. A RS-based study on changes in fractional vegetation cover in North Shaanxi and their driving factors[J]. Remote Sensing for Natural Resources, 2021, 33(4): 258-264.
[14] LIU Yongmei, FAN Hongjian, GE Xinghua, LIU Jianhong, WANG Lei. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.
[15] WANG Zheng, JIA Gongxu, ZHANG Qingling, HUANG Yue. Impacts of COVID-19 epidemic on the spatial distribution of GDP contributed by secondary and tertiary industries in Guangdong Province in the first quarter of 2020[J]. Remote Sensing for Natural Resources, 2021, 33(3): 184-193.
Viewed
Full text


Abstract

Cited

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