Research on building extraction rules based on SPOT6 data

doi:10.6046/gtzyyg.2017.03.09

Abstract
References
Related Articles
Metrics

Download: PDF(4057 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract For SPOT 6 satellite remote sensing image, a method based on rules was used to extract buildings. Firstly, the authors analyzed the extraction effects of every rule attribute and made the rule extract buildings based on the effect. Then the authors compared the methods of K-means clustering, K nearest neighbor (KNN), support vector machine (SVM) and neural network with the method used in this paper during the research. The precision evaluation of building extraction result shows that the accuracy of this method based on rules is higher than that of other methods. This method relieves the problems of the salt and pepper phenomenon and the same spectrum with foreign bodies, and provides some technical support for the wider application of SPOT 6 satellite images in the future.

Keywords NDVI remote sensing spatial distribution China

Issue Date: 15 August 2017

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	YAO Zhenhai
	QIU Xinfa
	SHI Guoping
	ZHANG Xiliang

Cite this article:

YAO Zhenhai,QIU Xinfa,SHI Guoping, et al. Research on building extraction rules based on SPOT6 data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 65-69.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.03.09 OR https://www.gtzyyg.com/EN/Y2017/V29/I3/65

[1] 江华.基于eCognition面向对象技术的高分辨率遥感影像土地利用分类——以福州琅岐岛为例[J].海峡科学,2014(8):12-17.
Jiang H.High resolution remote sensing image classification of land use based on eCognition object-oriented technology:Setting Lang Qi island of Fu Zhou as an example[J].Straits Science,2014(8):12-17.
[2] Uzar M,Yastikli N.Automatic building extraction using LiDAR and aerial photographs[J].Boletim de Ciências Geodésicas,2013,19(2):153-171.
[3] Mongus D,Luka c ˙ N,Žalik B.Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,93:145-156.
[4] 张景,姚凤梅,徐永明,等.基于MODIS的土地覆盖遥感分类特征的评价与比较[J].地理科学,2010,30(2):248-253.
Zhang J,Yao F M,Xu Y M,et al.Comparison and evaluation of classification features in land cover based on remote sensing[J].Scientia Geographica Sinica,2010,30(2):248-253.
[5] Shaker I F,Abd-Elrahman A,Abdel-Gawad A K,et al.Building extraction from high resolution space images in high density residential areas in the great Cairo region[J].Remote Sensing,2011,3(4):781-791.
[6] 陈洁丽,刘永学,李满春,等.基于归一化指数分析的居民地遥感信息提取[J].测绘科学,2010,35(2):204-206,177.
Chen J L,Liu Y X,Li M C,et al.Extracting remote sensing information of residential areas based on the analysis of normalized difference index[J].Science of Surveying and Mapping,2010,35(2):204-206,177.
[7] 陈洁丽,刘永学,李满春,等.一种基于遥感数据快速提取居民地信息的新方法[J].地理与地理信息科学,2010,26(5):72-75.
Chen J L,Liu Y X,Li M C,et al.A new method of extracting residential areas based on remote sensing image[J].Geography and Geo-Information Science,2010,26(5):72-75.
[8] 王惠英,苏伟,周军其.基于多分类器组合面向对象遥感影像信息提取方法[J].测绘工程,2009,18(5):22-26.
Wang H Y,Su W,Zhou J Q.Object-oriented information extraction method of remote sensing image based on multi-classifier mixed[J].Engineering of Surveying and Mapping,2009,18(5):22-26.
[9] 郑文武,曾永年.利用多分类器集成进行遥感影像分类[J].武汉大学学报:信息科学版,2011,36(11):1290-1293.
Zheng W W,Zeng Y N.Remote sensing imagery classification based on multiple classifiers combination algorithm[J].Geomatics and Information Science of Wuhan University,2011,36(11):1290-1293.
[10] 郭怡帆,张锦,卫东.面向对象的高分辨率遥感影像建筑物轮廓提取研究[J].测绘通报,2014(S2):300-303.
Guo Y F,Zhang J,Wei D.The research of object-oriented building contour extraction of high-resolution remote sensing image[J].Bulletin of Surveying and Mapping,2014(S2):300-303.
[11] 董小姣,张东水,李朝奎.面向对象的泰安市高分辨率影像住宅建筑物提取[J].地理空间信息,2013,11(1):67-69.
Dong X J,Zhang D S,Li C K.Object-oriented information extraction method of residential buildings from high resolution image in Tai’an[J].Geospatial Information,2013,11(1):67-69.
[12] 谭衢霖,高姣姣.面向对象分类提取高分辨率多光谱影像建筑物[J].测绘工程,2010,19(4):30-33,38.
Tan Q L,Gao J J.Building extraction from high resolution multispectral image using object-oriented classification method[J].Engineering of Surveying and Mapping,2010,19(4):30-33,38.
[13] 谭衢霖.高分辨率多光谱影像城区建筑物提取研究[J].测绘学报,2010,39(6):618-623.
Tan Q L.Urban building extraction from VHR multi-spectral images using object-based classification[J].Acta Geodaetica et Cartographica Sinica,2010,39(6):618-623.
[14] 张璐璐,何宁,徐成,等.基于K-means聚类的遥感影像条状地物半自动提取方法[J].北京联合大学学报,2015,29(1):47-52.
Zhang L L,He N,Xu C,et al.A strips features semi-automatic extraction method of remote sensing image based on K-means clustering[J].Journal of Beijing Union University,2015,29(1):47-52.
[15] 戚玉娇,李凤日.基于KNN方法的大兴安岭地区森林地上碳储量遥感估算[J].林业科学,2015,51(5):46-55.
Qi Y J,Li F R.Remote sensing estimation of aboveground forest carbon storage in Daxing’an mountains based on KNN method[J].Scientia Silvae Sinicae,2015,51(5):46-55.
[16] Turker M,Koc-San D.Building extraction from high-resolution optical spaceborne images using the integration of support vector machine(SVM) classification,Hough transformation and perceptual grouping[J].International Journal of Applied Earth Observation and Geoinformation,2015,34:58-69.
[17] 秦永,孔维华,曹俊茹,等.基于SVM的遥感影像土地利用变化检测方法[J].济南大学学报:自然科学版,2010,24(1):88-90.
Qin Y,Kong W H,Cao J R,et al.For land use remote sensing image change detection method based on SVM[J].Journal of University of Ji’nan:Science and Technology,2010,24(1):88-90.
[18] 卢柳叶,张青峰,李光录.基于BP神经网络的遥感影像分类研究[J].测绘科学,2012,37(6):140-143.
Lu L Y,Zhang Q F,Li G L.Image classification of remote sensing based on BP neural networks[J].Science of Surveying and Mapping,2012,37(6):140-143.
[19] 郭蕾,杨冀红,史良树,等.SPOT6遥感图像融合方法比较研究[J].国土资源遥感,2014,26(4):71-77.doi:10.6046/gtzyyg.2014.04.12"> doi:10.6046/gtzyyg.2014.04.12.
Guo L,Yang J H,Shi L S,et al.Comparative study of image fusion algorithms for SPOT6[J].Remote Sensing for Land and Resources,2014,26(4):71-77.doi:10.6046/gtzyyg.2014.04.12"> doi:10.6046/gtzyyg.2014.04.12.
[20] 马世斌,杨文芳,张焜.SPOT6卫星图像处理关键技术研究[J].国土资源遥感,2015,27(3):30-35.doi:10.6046/gtzyyg.2015.03.06"> doi:10.6046/gtzyyg.2015.03.06.
Ma S B,Yang W F,Zhang K.Study of key technology of SPOT6 satellite image processing[J].Remote Sensing for Land and Resources,2015,27(3):30-35.doi:10.6046/gtzyyg.2015.03.06"> doi:10.6046/gtzyyg.2015.03.06.
[21] Jiang H,Su Y Y,Jiao Q S,et al.Typical geologic disaster surveying in Wenchuan 8.0 earthquake zone using high resolution ground LiDAR and UAV remote sensing[C]//Proceedings SPIE 9262,Lidar Remote Sensing for Environmental Monitoring XIV.Beijing,China:SPIE,2014:926219.

[1]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3]	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.
[4]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[5]	HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
[6]	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.
[7]	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.
[8]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[9]	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.
[10]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[11]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[12]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[13]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[14]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[15]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.

Viewed

Full text

Abstract

Cited

Shared

Discussed