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国土资源遥感  2021, Vol. 33 Issue (1): 9-11    DOI: 10.6046/gtzyyg.2020170
     遥感水体及特定要素提取专栏 本期目录 | 过刊浏览 | 高级检索 |
遥感影像水体提取研究综述
苏龙飞(), 李振轩(), 高飞, 余敏
合肥工业大学土木与水利工程学院,合肥 230000
A review of remote sensing image water extraction
SU Longfei(), LI Zhengxuan(), GAO Fei, YU Min
School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230000, China
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摘要 

水是非常重要的资源,是一切人类与生物得以生存和发展的重要物质基础,水体提取便于了解现有的水资源概况,有助于对水资源合理的规划及治理,对人类的生活及社会活动具有重大影响。传统的人工方法费时费力,利用卫星遥感数据进行水体位置、面积、形状和河宽等水体参数的信息提取,已经成为一种快速获取水体参数的有效方法和手段。在广泛文献调研的基础上,阐述遥感影像水体提取的基本思想及其发展历程,对学者在水体提取的基本方法和现状进行较全面的综述,分析各种方法的优缺点,说明当前水体提取存在的问题和研究前景,使读者直观了解这一领域并为其研究提供一定思路。

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苏龙飞
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余敏
关键词 水体提取水体指数阈值法面向对象雷达    
Abstract

Water is a very important resource, and it is an important material basis for the survival and development of all human beings and organisms. Water extraction can result to easily understand the general situation of existing water resources, thus being conducive to the rational planning and management of water resources and having a significant impact on human life and social activities. Traditional artificial methods are time-consuming and laborious, and therefore satellite remote sensing data is now used to extract water parameters such as water position, area, shape and river width, which has become an effective method and means to obtain water parameters quickly. On the basis of extensive literature research, this paper illustrates the basic ideas of water extraction of remote sensing image and its development course as well as the basic method and current situation of water extraction performed by experts, and makes a comprehensive review and analysis of the advantages and disadvantages of various methods so as to explain the problems of water extraction and research prospect, make the readers understand the situation of this field and provide some ideas for the study in this field.

Key wordswater extraction    water index    threshold value method    object oriented    Radar
收稿日期: 2020-06-15      出版日期: 2021-03-18
ZTFLH:  P237  
基金资助:2018年安徽省测绘科技专项资金资助项目“3S支持下引江济淮工程生态环境监测”(CHZX201801);中央高校基本科研业务费专项资金资助项目“多源遥感影像变化检测研究”共同资助(JZ2019HGBZ0148)
通讯作者: 李振轩
作者简介: 苏龙飞(1994-),男,硕士研究生,主要研究方向为遥感影像信息提取。Email: 2357090110@qq.com
引用本文:   
苏龙飞, 李振轩, 高飞, 余敏. 遥感影像水体提取研究综述[J]. 国土资源遥感, 2021, 33(1): 9-11.
SU Longfei, LI Zhengxuan, GAO Fei, YU Min. A review of remote sensing image water extraction. Remote Sensing for Land & Resources, 2021, 33(1): 9-11.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020170      或      https://www.gtzyyg.com/CN/Y2021/V33/I1/9
Fig.1  水体提取方法分类
Fig.2  典型地物的光谱特征曲线[5]
植被指数 模型公式 特点
归一化差异水体指数(NDWI) NDWI=(G-NIR)/(G+NIR) 能够抑制植被信息,突出水体; 对建筑物和土壤的分离有一定影响; 受冰雪、薄云和山体阴影影响较大,适用于地势平坦地区
归一化差异水体指数(NDWI3)[14] NDWI3=(NIR-MIR)/(NIR+MIR) 在城镇区域应用较好; 受山体阴影影响较大
改进的归一化差异水体指数(MNDWI)[15] MNDWI=(G-MIR)/(G+MIR) 能够较好地去除居民地和土壤等影响,突出水体; 受阴影影响较大
增强水体指数(EWI)[16] EWI=(G-NIR-SWIR)/(G+NIR+SWIR) 能够抑制居民地、土壤和植被等噪声; 易受到阴影及浅滩的影响; 适合半干旱地区的水体提取
新型水体指数(NWI)[17] NWI=[B-(NIR+MIR+FIR)]/[B+NIR+
MIR+FIR]
能够较好地区分水体和阴影、土壤和建筑物的影响,受噪声影响较小,普遍适用于一般地区,且提取精度较高
修订归一化差异水体指数(RNDWI)[18] RNDWI=(SWIR-R)/(SWIR+R) 能削弱混合像元和山体阴影的影响,较好地提取水陆边界; 一般适用于山区等地形起伏较大地区
Gauss归一化差异水体指数(GNDWI)[19] GNDWIi,j=NDWIi,j-NDWI 针对线状河流水体的精确提取,可较好地保留水体的完整性; 受云和阴影的影响较大,需要用到高程信息,实现过程相对比较麻烦,但提取精度较高
自动水体提取指数AWEInsh,(AWEIsh)[20] AWEInsh=4(ρG-ρSWIR1)-(0.25ρNIR+
2.75ρSWIR2)
AWEIsh=ρB+2.5ρG-1.5(ρNIR+ρSWIR1)- 0.25ρSWIR2
能够抑制地形阴影和暗表面; 受冰雪等高反射表面影响较大
植被指数 模型公式 特点
伪归一化水体差异指数FNDWI[21] FNDWI=(FG-NIR)/(FG+NIR) 较好地区分水体与建筑物,易受冰雪、薄云和山体阴影影响,不适用于山区等地形起伏较大区域
阴影水体指数SWI[22] SWI=B+G-NIR 较好地区分水体和阴影,能削弱积雪和山体裸地的影响,适用于山区的水体提取
Tab.1  几种常用的水体指数及其模型
[1] Yang C J, Wei Y M, Wang S Y, et al. Extracting the flood extent from satellite SAR image with the sup-port of topographic data[C]// Proceedings of 2001 International Conferences on Info-Tech and Info-Net, 2001.
[2] 许杰玉, 赵晓飞, 吕春英, 等. 星云湖流域水环境污染特征分析与综合整治研究[C]// 中国环境科学学会会议论文, 2015.
Xu J Y, Zhao X F, Lyu C Y, et al. Analysis of water environmental pollution characteristics and comprehensive remediation in Xingyun Lake Basin[C]// Annual Meeting of Chinese Society for Environmental Sciences, 2015.
[3] 杜云艳, 周成虎. 水体的遥感信息自动提取方法[J]. 遥感学报, 1998,2(4):264-269.
Du Y Y, Zhou C H. Automatically extracting remote sensing information of water bodyies[J]. Journal of Remote Sensing, 1998,2(4):264-269.
[4] 周成虎, 骆剑承, 杨晓梅. 遥感影像地学理解与分析[M]. 北京: 科学出版社, 1999.
Zhou C H, Luo J C, Yang X M. Remote sensing image geoscience understanding and analysis[M]. Beijing: Science Press, 1999.
[5] Mcfeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996,17(7):1425-1432.
[6] 崔齐, 王杰, 汪闽, 等. 矢量约束的面向对象高分遥感影像水体提取[J]. 遥感信息, 2018,33(4):115-121.
Cui Q, Wang J, Wang M, et al. Water extraction from high-resolution remote sensing imagery based on vector data constraint and object-based image analysis[J]. Remote Sensing Information, 2018,33(4):115-121.
[7] 王航, 秦奋. 遥感影像水体提取研究综述[J]. 测绘科学, 2018,43(5):23-32.
Wang H, Qin F. Summary of the research on water body extraction and application from remote sensing image[J]. Surveying and Mapping Science, 2018,43(5):23-32.
[8] 李丹, 吴保生, 陈博伟, 等. 基于卫星遥感的水体信息提取研究进展与展望[J]. 清华大学学报, 2020,60(2):147-161.
Li D, Wu B S, Chen B W, et al. Review of water body information extraction based on satellite remote sensing[J]. Journal of Tsinghua University, 2020,60(2):147-161.
[9] 马延辉, 林辉, 孙华, 等. 基于CIWI模型的水体信息提取研究[J]. 中国水土保持, 2009(5):41-43.
Ma Y H, Lin H, Sun H, et al. Research on water information extraction based on CIWI model[J]. Soil and Water Conservation in China, 2009(5):41-43.
[10] 都金康, 黄永胜, 冯学智, 等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报, 2001,5(3):214-219.
Du J K, Huang Y S, Feng X Z, et al. Study on water bodies extraction and classification from SPOT image[J]. Journal of Remote Sensing, 2001,5(3):214-219.
[11] Frazier P S, Page K J. Water body detection and delineation with Landsat TM data[J]. Photogrammetric Engineering and Remote Sensing, 2000,66(12):1461-1467.
[12] 毕海芸, 王思远, 曾江源, 等. 基于TM影像的几种常用水体提取方法的比较和分析[J]. 遥感信息, 2012,27(5):77-82.
Bi H Y, Wang S Y, Zeng J Y, et al. Comparison and analysis of several common water extraction methods based on TM image[J]. Remote Sensing Information, 2012,27(5):77-82.
[13] 汪金花, 张永彬, 孔改红. 谱间关系法在水体特征提取中的应用[J]. 矿山测量, 2004(4):30-32.
Wang J H, Zhang Y B, Kong G H. Application of spectral relationship method in water feature extraction[J]. Mine Surveying, 2004(4):30-32.
[14] Ouma Y O, Tateishi R. A water index for rapid mapping of shore line changes of five East African Rift Valey Lakes:An empirical analysis using Landsat TM and ETM+data[J]. International Journal of Remote Sensing, 2006,27(15):3153-3181.
doi: 10.1080/01431160500309934
[15] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005,9(5):589-595.
doi: 10.11834/jrs.20050586
Xu H Q. A study on information extraction of water body with the modified normalized difference water index (MNDWI)[J]. Journal of Remote Sensing, 2005,9(5):589-595.
doi: 10.11834/jrs.20050586
[16] 丁凤. 一种基于遥感数据快速提取水体信息的新方法[J]. 遥感技术与应用, 2009,24(2):167-171.
Ding F. A new method for fast information extraction of water bodies using remotely sensed data[J]. Remote Sensing Technology and Application, 2009,24(2):167-171.
[17] 曹荣龙, 李存军, 刘良云, 等. 基于水体指数的密云水库面积提取及变化监测[J]. 测绘科学, 2008,33(2):158-160.
Cao R L, Li C J, Liu L Y, et al. Extracting Miyun Reservoir’s water area and monitoring its change based on a revised normalized different water index[J]. Surveying and Mapping Science, 2008,33(2):158-160.
[18] 闫霈, 张友静, 张元. 利用增强型水体指数(EWI)和GIS去噪音技术提取半干旱地区水系信息的研究[J]. 遥感信息, 2007(6):62-67.
Yan P, Zhang Y J, Zhang Y. A study on information extraction of water system in semi-arid regions with the enhanced water index(EWI) and GIS based noise remove techniques[J]. Remote Sensing Information, 2007(6):62-67.
[19] 沈占锋, 夏列钢, 李均力, 等. 采用高斯归一化水体指数实现遥感影像河流的精确提取[J]. 中国图象图形学报, 2013,18(4):421-428.
Shen Z F, Xia L G, Li J L, et al. Automatic and high-precision extraction of rivers from remotely sensed images with Gaussian normalized water index[J]. Chinese Journal of Images and Graphics, 2013,18(4):421-428.
[20] Feyisa G L, Meilb Y H, Fensholt R, et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014,140:23-35.
[21] 周艺, 谢光磊, 王世新, 等. 利用伪归一化差异水体指数提取城镇周边细小河流信息[J]. 地球信息科学学报, 2014,16(1):102-107.
Zhou Y, Xie G L, Wang S X, et al. Information extraction of thin rivers around built-up lands with false NDWI[J]. Journal of Earth Information Science, 2014,16(1):102-107.
[22] 陈文倩, 丁建丽, 李艳华, 等. 基于国产GF-1遥感影像的水体提取方法[J]. 资源科学, 2015,37(6):1166-1172.
Chen W J, Ding J L, Li Y H, et al. Extraction of water information based on China-made GF-1 remote sense image[J]. Resources Science, 2015,37(6):1166-1172.
[23] Vapnik V N. The nature of statistical learning theory[M]. New York:Springer Verlag, 2000,37(3):988-999.
[24] Vapnik V N. A nover view of statistical learning theory[J]. IEEE Transactionson Neural Networks, 1999,10(5):988-999.
doi: 10.1109/72.788640 pmid: 18252602
[25] Roli F, Fumera G. Support vector machines for remote sensing image classification[C]// Image and Signal Processing for Remote Sensing VI, 2001: 160-166.
[26] 段秋亚, 孟令奎, 樊志伟, 等. GF-1卫星影像水体信息提取方法的适用性研究[J]. 国土资源遥感, 2015,27(4):79-84.doi: 10.6046/gtzyyg.2015.04.13.
Duan Q Y, Meng L K, Fan Z W, et al. Applicability of the water information extraction method based on GF-1 image[J]. Remote Sensing of Land Resources, 2015,27(4):79-84.doi: 10.6046/gtzyyg.2015.04.13.
[27] Paul A, Tripath I D, Dutta D. Application and comparison of advanceds upervised classifiersin extraction of water bodies from remote sensing images[J]. Sustainable Water Resources Management, 2018,4(4):905-919.
[28] 张德军, 杨世琦, 王永前, 等. 基于GF-1数据的三峡库区水体信息精细化提取[J]. 人民长江, 2019,50(9):233-239.
Zhang D J, Yang S Q, Wang Y Q, et al. Refined water body information extraction of Three Gorges Reservoir by using GF-1 satellite data[J]. People’s Yangtze River, 2019,50(9):233-239.
[29] Li D, Cheng T. KDG knowledge discovery from GIS:Roosition sonthe use of KDD inanintelient GIS[Z]. 1994.
[30] Quinlan J R. Simplifying decisiontrees[J]. International Journal of Man Machine Studies, 1999,27(3):221-234.
doi: 10.1016/S0020-7373(87)80053-6
[31] 沙占江, 曾永年, 马海州, 等. 遥感和GIS支持下的龙羊峡库区土地沙漠化动态研究[J]. 中国沙漠, 2000,20(1):51-54.
Sha Z J, Zeng Y N, Ma H Z, et al. Dynamic monitoring of desertification with RS and GIS in Longyangxia Reservoir Area[J]. China Desert, 2000,20(1):51-54.
[32] 陈超, 傅姣琪, 随欣欣, 等. 面向灾后水体遥感信息提取的知识决策树构建及应用[J]. 遥感学报, 2018,22(5):792-801.
Chen C, Fu J Q, Sui X X, et al. Construction and application of knowledge decision tree after a disaster for water body information extraction from remote sensing images[J]. Journal of Remote Sensing, 2018,22(5):792-801.
[33] Tehrany M S, Pradhan B, Jebur M N. Remote sensing data reveals ecoenvironmental changes in urban areas of Klang valey,Malaysia:Contribution from object based analysis[J]. Indian Society of Remote Sensing, 2013,41(4):1-11.
[34] Cheng Q, Chen J F. Research on the extraction method of landcover informationin southern coastal land of Hangzhou bay based on GF-1 image[J]. Journal of Natural Resources, 2015,30(2):350-360.
[35] Yu Q. Object based detail edvegetation mapping using high spatial resolution imagery[J]. Photogrammetric Engineering and Remote Sensing, 2006,72(7):799-811.
[36] 张真鲜. 新疆特克斯河流域生态环境遥感监测与评价[D]. 北京:中国地质大学(北京), 2001.
Zhang Z X. Eco-environmental monitoring and evaluation of Tekes Watershed in Xinjiang using remote sensing images[D]. Beijing:China University of Geosciences (Beijing), 2001.
[37] 吴小娟, 肖晨超, 崔振营, 等. "高分二号"卫星数据面向对象的海岸线提取法[J]. 航天返回与遥感, 2015,36(4):84-92.
Wu X J, Xiao C C, Cui Z Y, et al. Coastline extraction based on object-oriented method using GF-2 satellite data[J]. Space Return and Remote Sensing, 2015,36(4):84-92.
[38] 毛莹莹, 陈友飞. 基于面向对象法的Landsat8影像山区细小水体提取方法[J]. 亚热带资源与环境学报, 2015,10(4):86-92.
Mao Y Y, Chen Y F. An object-oriented method based on the extraction of fine water from Landsat8 image mountains[J]. Journal of Subtropical Resources and Environment, 2015,10(4):86-92.
[39] 徐涛, 谭宗坤, 闫小平. 面向对象的城市水体信息提取方法[J]. 地理空间信息, 2010,8(3):64-66.
Xu T, Tan Z K, Yan X P. Extraction techniques of urban water bodies based on object-oriented[J]. Geospatial Information, 2010,8(3):64-66.
[40] 王俊海, 阮仁宗, 柴颖, 等. 基于高分二号的面向对象城市水体信息提取[J]. 地理空间信息, 2018,16(9):34-40.
Wang J H, Ruan R Z, Chai Y, et al. Object-oriented urban water information extraction based on GF-2[J]. Geospatial Information, 2018,16(9):34-40.
[41] 莫伟华, 孙涵, 钟仕全, 等. MODIS水体指数模型(CIWI)研究及其应用[J]. 遥感信息, 2007(5):15-23.
Mo W H, Sun H, Zhong S Q, et al. Research on the CIWI model and its application[J]. Remote Sensing Information, 2007(5):15-23.
[42] Sui H G, Chen G L. An automatic integrated image segmentation,registration and change detection method for water body extraction using HSR images and GIS data[J]. International Archives of the Photo Grammetry,Remote Sensing and Spatial, 2013,40(7):237-242.
[43] 杨旭, 陈建国, 程潭武, 等. 基于RS与GIS技术的数字流域水体信息的提取[J]. 水资源与水工程学报, 2018,29(4):81-86.
Yang X, Chen J G, Cheng T W, et al. The extraction of digital watershed water body information based on GIS and RS[J]. Journal of Water Resources and Water Engineering, 2018,29(4):81-86.
[44] 王铭. 基于混合像元分解的东北地区时令水体提取及变化监测[D]. 长春:中国科学院大学(中国科学院东北地理与农业生态研究所), 2017.
Wang M. The temporary water bodies extraction and monitoring in northeast China through spectral mixture analysis[D]. Changchun:University of Chinese Academy of Sciences, 2017.
[45] 孔美美. 基于混合像元分解的水体提取及变化监测研究[D]. 泉州:华侨大学, 2016.
Kong M M. Reservoir surface water extraction and monitoring based on pixel unmixing[D]. Quanzhou:Huaqiao University, 2016.
[46] 杨文亮, 杨敏华, 祁洪霞. 利用BP神经网络提取TM影像水体[J]. 测绘科学, 2012,32(1):148-150.
Yang W L, Yang M H, Qi H X. Water body extracting from TM image based on BPNN[J]. Science of Surveying and Mapping, 2012,32(1):148-150.
[47] 王知音. 基于机器学习的遥感图像水体提取研究[D]. 乌鲁木齐:新疆大学, 2016.
Wang Z Y. Research on water body extraction from remote sensing image based on machine learning[D]. Urumqi:Xinjiang University, 2016.
[48] 李士进, 王声特. 基于混合特征空间MRF(Markov Random Filed)模型的高分辨率遥感影像水体提取[J]. 南京师大学报, 2017,40(1):13-19.
Li S J, Wang S T. A new algorithm based on hybrid feature space MRF( Markov random filed) model for water information extraction from high resolution remote sensing imagery[J]. Journal of Nanjing Normal University, 2017,40(1):13-19.
[49] 唐德可, 王峰, 王宏琦. 基于马尔科夫分割的单极化SAR数据洪涝水体检测方法[J]. 电子与信息学报, 2019,41(3):619-625.
Tang D K, Wang F, Wang H Q. Single-polarization SAR data flood water detection method based on Markov segmentation[J]. Journal of Electronics and Information Technology, 2019,41(3):619-625.
[50] 齐庆超, 张小磊, 金江峰, 等. 基于光谱角匹配算法的水体信息提取研究[J]. 测绘与空间地理信息, 2016,39(2):93-96.
Qi Q C, Zhang X L, Jin J F, et al. Water extraction based on spectral angle matching algorithm[J]. Mapping and Spatial Geographic Information, 2016,39(2):93-96.
[51] 张伟, 赵理君, 郑柯, 等. 一种改进光谱角匹配的水体信息提取方法[J]. 测绘通报, 2017(10):34-38.
Zhang W, Zhao L J, Zheng K, et al. An improved method for water information extraction using MSAM[J]. Bulletin of Surveying and Mapping, 2017(10):34-38.
[52] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006,18(7):1527.
doi: 10.1162/neco.2006.18.7.1527 pmid: 16764513
[53] 杜敬. 基于深度学习的湖泊湿地信息提取及时空演变特征研究[D]. 上海:华东理工大学, 2017.
Du J. Deep learning based extraction and spatial-temporal evolution of lake wetland[D]. Shanghai:East China University of Science and Technology, 2017.
[54] Längkvist M, Kiselev A, Alirezaie M, et al. Classification and segmentation of satellite orthoimagery using convolutional neural network[J]. Remote Sensing, 2016,8(4):329.
[55] 赵文, 杜生. 学习多尺度、更深层次的遥感分类方法[J]. 摄影与遥感学报, 2016(113):155-165.
Zhao W, Du S. Learning multi-scale and deeper remote sensing classification methods[J]. Journal of Photography and Remote Sensing, 2016(113):155-165.
[56] 王雪, 隋立春, 钟棉卿, 等. 全卷积神经网络用于遥感影像水体提取[J]. 测绘通报, 2018(6):41-45.
Wang X, Sui L C, Zhong M Q, et al. Fully convolution neural networks for water extraction of remote sensing images[J]. Bulletin of Surveying and Mapping, 2018(6):41-45.
[57] 梁泽毓. 基于深度学习的多元遥感水体信息提取方法及其应用研究[D]. 合肥:安徽大学, 2019.
Liang Z Y. Research on water information extraction mathod of multi-source remote sensing based on deep learning and its application[D]. Hefei:Anhui University, 2019.
[58] 曹云刚, 刘闯. EnviSat ASAR数据在水情监测中的应用[J]. 地理与地理信息科学, 2006,22(2):13-15.
Cao Y G, Liu C. Study on flood monitoring using EnviSat ASAR data[J]. Geography and Geographic Information Science, 2006,22(2):13-15.
[59] 申邵洪, 谭德宝, 陈蓓青. 基于KI算法的多时相ASAR影像水面信息变化监测[J]. 长江科学院院报, 2008,25(2):29-32.
Shen S H, Tan D B, Chen B Q. Change detection of water surface in multitemporal ASAR images based on KI algorithm[J]. Journal of Changjiang Academy of Sciences, 2008,25(2):29-32.
[60] 李景刚, 黄诗峰, 李纪人. EnviSAT卫星先进合成孔径雷达数据水体提取研究——改进的最大类间方差阈值法[J]. 自然灾害学报, 2010,19(3):139-145.
Li J G, Huang S F, Li J R. Research on extraction of water body from EnviSAT ASAR images:A modified Otsu threshold method[J]. Journal of Natural Disasters, 2010,19(3):139-145.
[61] 杨存建, 魏一鸣, 王思远, 等. 基于DEM的SAR图像洪水水体的提取[J]. 自然灾害学报, 2002,11(3):121-125.
Yang C J, Wei Y M, Wang S Y, et al. Extracting the flood extent from SAR imagery on basis of DEM[J]. Journal of Natural Disasters, 2002,11(3):121-125.
[62] Hong S, Jang H, Kim N, et al. Water area extraction using RADARSAT SAR imagery combined with Landsat imagery and terrain information[J]. Sensors, 2015,15(3):6652-6667.
doi: 10.3390/s150306652 pmid: 25808768
[63] 王栋, 陈映鹰, 秦平. SAR影像水体目标提取的序列非线性波波方法[J]. 同济大学学报, 2009,5(13):234-236.
Wang D, Chen Y Y, Qin P. Method for water object extraction in SAR imagery based on sequential nonlinear filtering[J]. Journal of Tongji University, 2009,5(13):234-236.
[64] Klemenja K S, Waske B, Valero S, et al. Automatic detection of riversin high Gresolution SAR data[J]. IEEE Journal of Selected Topicsin Applied Earth Observations and Remote Sensing, 2012,5(5):1364-1372.
[65] Lyu W T, Yu Q Z, Yu W X. Water extraction in SAR images using GLCM and support vector machine[C]// International Conference on Signal Processing, 2010: 740-743.
[66] 胡德勇, 李京, 陈云浩, 等. 单波段单极化SAR图像水体和居民地信息提取方法研究[J]. 中国图象图形学报, 2008,13(2):257-263.
Hu D Y, Li J, Chen Y H, et al. Water and settlement area extraction from single-band,single-polarization SAR images based on SVM method[J]. Chinese Journal of Graphic Images, 2008,13(2):257-263.
[67] Zeng C Q, Wang J F, Huang X D, et al. Urban water body detection from the combination of high resolution optical and SAR images[C]// Proceedings of 2015 Joint Urban Remote Sensing Event.Lausanne,Switzerland, 2015.
[68] Irwin K, Beaulne D, Braun A, et al. Fusion of SAR,optical imagery and airborne LiDAR for surface water detection[J]. Remote Sensing, 2017,9(9):890.
[69] 曹荣龙, 李存军, 刘良云, 等. 基于水体指数的密云水库面积提取及变化监测[J]. 测绘科学, 2008,33(2):158-160.
Cao R L, Li C J, Liu L Y, et al. Extracting Miyun Reservoir’s water area and monitoring its change based on a revised normalized different water index[J]. Science of Surveying and Mapping, 2008,33(2):158-160.
[70] 申文明, 王文杰, 罗海江, 等. 基于决策树分类技术的遥感影像分类方法研究[J]. 遥感技术与应用, 2007,22(3):333-338.
Shen W M, Wang W J, Luo H J, et al. Classification methods of remote sensing image based on decision tree technologies[J]. Remote Sensing Technology and Application, 2007,22(3):333-338.
[71] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(5786):504-507.
pmid: 16873662
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