高分影像复杂背景下的城市水体自动提取方法

doi:10.11896/jsjkx.181001985

摘要/Abstract

摘要： 城市水体分布信息对于理解城市水循环、热岛效应等地理现象具有重要意义。利用高分辨率影像进行水体提取和水体制图是常用的信息获取方式。由于城市环境背景复杂、高分影像光谱通道少以及水体在影像上分布比例不均匀等原因,将高分影像应用于水体自动提取仍存在较大难度。对此,基于国产高分影像发展一种面向复杂环境的城市水体自动化提取方法。首先,根据水体近红外通道灰度值较低的特征,自适应选取阈值进行分割,获取初始水体;其次,对初始水体进行缓冲以得到靶区域,使用高斯混合模型来表达其整体分布,通过改进期望最大算法估计水体类别分布参数后,使用最大似然法进行水体自动提取;在此基础上,针对粗提取水体中混杂阴影的问题,提出了融合特征方法来去除阴影,从而获得准确的水体提取结果。对上海市金山区的水体提取实验表明,使用所提方法可以有效提取实验影像中占比较小的水体结构,整体精度较目前常用的自动提取算法有明显提升。

关键词: 城市水体提取, 改进EM算法, 高斯混合模型, 类别不均衡, 阴影去除

Abstract: The distribution of urban water bodies is of great significance for people to understand the geographical phenomena such as the urban water circulation and the Heat-island Effect.It is common to obtain information by using high-resolution images for water extraction and water mapping.However,automatically extraction of water bodies by using the high-resolution images still is difficult for the complex background of the urban area,fewer spectral channels provided by the high-resolution images and the uneven distribution of water bodies in the images.This paper proposed an automatic extraction method of urban water bodies in complex background based on high-resolution images.First,adaptive threshold is selected for segmentation to gain the initial region of water,since water has a low gray value of the near infrared channel.Next,on the initial region,a buffering algorithm are used to obtain the target region of water extraction,and gauss mixture model and an expectation maximization algorithm is used to improve the distributionpara-meters of water.Then,the water bodies are extracted automatically using the maximum likelihood method with these parameters.As for the large number of shadow elements mixed in the rough extraction,a fusion features method is proposed to eliminate those noise points and obtain more accurate extraction result.The experiment results of water extraction in Jinshan show that the proposed method can effectively extract the structure of water bodies with small proportion in the experimental images,and perform well with high accuracy comparing to the commonly used automatic extraction algorithms.

Key words: Class imbalance, Extraction of urban water bodies, Gauss mixture model, Modified expectation maximization algorithm, Shadow removal

中图分类号:

TP751

王卫红, 陈骁, 吴炜, 高星宇. 高分影像复杂背景下的城市水体自动提取方法[J]. 计算机科学, 2019, 46(11): 277-283. https://doi.org/10.11896/jsjkx.181001985

WANG Wei-hong, CHEN Xiao, WU Wei, GAO Xing-yu. Method of Automatically Extracting Urban Water Bodies from High-resolution Images with Complex Background[J]. Computer Science, 2019, 46(11): 277-283. https://doi.org/10.11896/jsjkx.181001985

参考文献

[1]GHOSH S,DAS A.Modelling urban cooling island impact ofgreen space and water bodies on surface urban heat island in a continuously developing urban area[J].Modeling Earth Systems and Environment,2018,4(2):501-515.
[2]BIORESITA F,PUISSANT A,STUMPF A,et al.A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery[J].Remote Sensing,2018,10(2):217-233.
[3]ZHAO X,WANG P,CHEN C,et al.Waterbody information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge[J].International journal of remote sensing,2017,38(5):1404-1422.
[4]BEHERA M D,GUPTA A K,BARIK S K,et al.Use of satelliteremote sensing as a monitoring tool for land and water resources development activities in an Indian tropical site[J].Environmental Monitoring and Assessment,2018,190(7):401.
[5]APPEL I.Uncertainty in satellite remote sensing of snow fraction for water resources management[J].Frontiers of Earth Science,2018,12(4):711-727.
[6]MAHDIANPARI M,SALEHI B,MOHAMMADIMANESH F,et al.Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery[J].Remote Sensing of Environment,2018,206:300-317.
[7]JIA K,JIANG W,LI J,et al.Spectral matching based on discrete particle swarm optimization:A new method for terrestrial water body extraction using multi-temporal Landsat 8 images[J].Remote Sensing of Environment,2018,209:1-18.
[8]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.
[9]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.(in Chinese)
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.
[10]WANG X,XIE S,ZHANG X,et al.A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery[J].International Journal of Applied Earth Observation & Geoinformation,2018,68:73-91.
[11]HUA L,MAN W,WANG Q,et al.A new decision tree classification approach for extracting urban land from Landsat TM in a coastal city,China[C]∥2012 International Symposium on Information Science and Engineering (ISISE).New York:IEEE,2012:282-286.
[12]SUN X,LI L,ZHANG B,et al.Soft urban water cover extraction using mixed training samples and Support Vector Machines[J].International Journal of Remote Sensing,2015,36(13):3331-3344.
[13]SCARPA G,GARGIULO M,MAZZA A,et al.A CNN-BasedFusion Method for Feature Extraction from Sentinel Data[J].Remote Sensing,2018,10(2):236.
[14]MIAO Z,FU K,SUN H,et al.Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks[J].IEEE Geoscience and Remote Sensing Letters,2018,15(4):602-606.
[15]HE H Q,DU J,CHEN T,et al.Remote sensing image waterbody extraction combing NDWI with Convolutional Neural Network[J].Remote Sensing Information,2017,32(5):82-86.(in Chinese)
何海清,杜敬,陈婷,等.结合水体指数与卷积神经网络的遥感水体提取[J].遥感信息,2017,32(5):82-86.
[16]LUO J C,ZHOU C H,LIANG Y,et al.Finite mixture modeland its EM clustering algorithm for remote sensing data[J].Journal of Image and Graphics,2002,7(4):336-340.(in Chinese)
骆剑承,周成虎,梁怡,等.有限混合密度模型及遥感影像EM聚类算法[J].中国图象图形学报,2002,7(4):336-340.
[17]DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society:Series B (Methodological),1977,39(1):1-22.
[18]ZHANG F,LI J,BING Z,et al.A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images[J].International Journal of Remote Sensing,2018,39(11):3429-3451.
[19]ROSIN P L.Unimodal thresholding[J].Pattern Recognition,2001,34(11):2083-2096.
[20]HUANG X,ZHANG L P,LI P X.Classification of high spatial resolution remotely sensed imagery based on the fusion of spectral and shape features[J].Journal of Remote Sensing,2007,11(2):193-200.(in Chinese)
黄昕,张良培,李平湘.融合形状和光谱的高空间分辨率遥感影像分类[J].遥感学报,2007,11(2):193-200.
[21]REN G B,ZHANG J,MA Y,et al.Generative model basedsemi-supervised learning method of remote sensing image classification[J].Journal of Remote Sensing,2010,14(6):1090-1104.(in Chinese)
任广波,张杰,马毅,等.生成模型学习的遥感影像半监督分类[J].遥感学报,2010,14(6):1090-1104.
[22]LIU S,SA R,MAGUIRE O,et al.Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model[C]∥Medical Imaging 2015:Image Processing.UnitedStates:International Society for Optics and Photonics,2015:94132H.
[23]BOCHOW M,HEIM B,KUSTER T,et al.Automatic detection and delineation of surface water bodies in airborne hyperspectral data[J].Geoscience and Remote Sensing Symposium,2012,53(60):5226-5229.
[24]CHEN Y,WEN D,JING L,et al.Shadow information recovery in urban areas from very high resolution satellite imagery[J].International Journal of Remote Sensing,2007,28(15):3249-3254.
[25]GUO J H,TIAN Q J,WU Y Z,et al.Study on multispectral detecting shadow areas and a theoretical model of removing sha-dows from remote sensing images[J].Journal of Remote Sen-sing,2006,10(2):151-159.(in Chinese)
虢建宏,田庆久,吴昀昭.遥感影像阴影多波段检测与去除理论模型研究[J].遥感学报,2006,10(2):151-159.
[26]ONO A,KAJIWARA K,HONDA Y.Development of new vegetation indexes,shadow index (SI) and water stress trend (WST)[J].Networking the World with Remote Sensing,2010,38(1):710-714.
[27]JIA K,LIANG S,GU X,et al.Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data[J].Remote Sensing of Environment,2016,177:184-191.
[28]SHEN G,XU B,JIN Y,et al.Monitoring wind farms occupying grasslands based on remote-sensing data from China’s GF-2 HD satellite—A case study of Jiuquan city,Gansu Province,China[J].Resources,Conservation and Recycling,2017,121:128-136.
[29]ÜNSALAN A C,BOYER K L.Linearized Shadow and Water Indices[J].Advances in Computer Vision & Pattern Recognition,2011,21(5):41-46.
[30]CAI D,LI M,BAO Z,et al.Study on shadow detection method on high resolution remote sensing image based on HIS space transformation and NDVI index[C]∥2010 18th International Conference on Geoinformatics.New York:IEEE,2010:1-4.
[31]PAL M,MATHER P M.Support Vector Machines for classification in remote sensing[J].International Journal of Remote Sensing,2015,26(5):1007-1011.
[32]ZHU C,SHI W,PESARESI M,et al.The recognition of roadnetwork from high-resolution satellite remotely sensed data using image morphological characteristics[J].International Journal of Remote Sensing,2005,26(24):5493-5508.
[33]YANG L,SU L H,WU B G,et al.Target Detection Algorithm of Hyperspectral Remote Sensing Imagery Combined with CEM.Journal of Chongqing University of Technology(Natural Science),2017,31(12):146-150,172.(in Chinese)
杨磊,苏令华,吴宝刚,等.一种结合CEM的高光谱遥感影像目标检测算法.重庆理工大学学报(自然科学),2017,31(12):146-150,172.
[34]JIA H L,DAI H X.Remote sensing image classification based on local partial classifier and deep neural network.Machine Tool & Hydraulics,2017,45(24):64-68,89.(in Chinese)
贾厚林,戴红霞.基于局部分类器和深度神经网络的遥感图像分类.机床与液压,2017,45(24):64-68,89.

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