Please wait a minute...
img

官方微信

遥感技术与应用  2017, Vol. 32 Issue (4): 709-713    DOI: 10.11873/j.issn.1004-0323.2017.4.0709
遥感应用     
基于稀疏分解和改进MRF模型的SAR海冰图像分割方法
张宝华,周文涛,吕晓琪
(内蒙古科技大学信息工程学院,内蒙古 包头014010 )
SAR Sea Ice Image Segmentation Method based on Low Rank Sparse Representation and Improved MRF Model
Zhang Baohua,Zhou Wentao,Lu Xiaoqi
(Information Engineering School,Inner Mongolia University of Science
and Technology,Baotou 014010,China )
 全文: PDF 
摘要:
合成孔径雷达(SAR)海冰图像的精确分割是准确解译海冰分布信息的前提,但现有分割方法受相干斑噪声影响严重,分割误差大,解译结果可靠性低。提出一种基于低秩稀疏表示的SAR海冰图像分割方法,首先利用噪声分布的稀疏性,通过鲁棒性主成分分析提取图像的稀疏分量,再利用双边滤波增强图像细节信息。针对基于固定势函数的MRF分割模型无法准确反映图像区域间关联性的问题,根据贝叶斯置信传播算法建立基于交互势函数的 MRF分割模型准确分割海冰图像。利用Radarsat系列卫星数据验证算法性能,结果表明:和传统算法相比,本文算法在保持分割图像连通性的同时,能增强图像的细节信息,具有更高的分割精度。
关键词: 海冰SAR噪声抑制低秩稀疏表示    
Abstract: Accurate segmentation of Synthetic Aperture Radar (SAR)images is the premise of interpreting the distribution information of sea ice.However the existing segmentation methodsare seriously interfered by speckle noise,which leads to high segmentation error and low reliability interpreting results.In this paper,a novel sea ice SAR image segmentation method based on low rank sparse representation is proposed,firstly sparse components are extracted from the source image by using robust principal component,and then bilateral filter is used to enhance the image details.Due to the MRF segmentation model based on fixed potential function cannot accurately reflect the relevance between the areas,MRF segmentation model based on interactive potential function is built to segment the sea ice image accurately.A series of Radarsat satellites data are tested to validate performance of the proposed method,the results show that compare with traditional segmentation algorithms,the proposed method algorithm can not only maintain the connectivity of the image better,but also has higher segmentation accuracy.

Key words: Sea ice    SAR    Noise suppression    Low rank sparse representation
收稿日期: 2017-01-14 出版日期: 2017-09-13
:  TP751.1  
基金资助: 国家自然科学基金项目(61261028),国家海洋局海洋遥测工程技术研究中心创新青年基金(2014003),内蒙古自治区高等学校“青年科技英才支持计划”青年科技骨干项目(NJYT\|14\|B11),内蒙古自然科学基金项目(2014MS0610),内蒙古科技大学创新基金(2014QNGG07),内蒙古科技大学教改项目(YJSJGX2015006)资助。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
张宝华
周文涛
吕晓琪

引用本文:

张宝华,周文涛,吕晓琪. 基于稀疏分解和改进MRF模型的SAR海冰图像分割方法[J]. 遥感技术与应用, 2017, 32(4): 709-713.

Zhang Baohua,Zhou Wentao,Lu Xiaoqi. SAR Sea Ice Image Segmentation Method based on Low Rank Sparse Representation and Improved MRF Model. Remote Sensing Technology and Application, 2017, 32(4): 709-713.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2017.4.0709        http://www.rsta.ac.cn/CN/Y2017/V32/I4/709

[1]Zhang Y H,Mohamed.Unsupervised Segmentation of Highly Dynamic Scenes through Global Optimization of Multiscale Cues[J].Pattern Recognition,2015,48(11):3477-3487.
[2]Karadag O O,Fatos T.Image Segmentation by Fusion of Low Level and Domain Specific Information via Markov Random Fields[J].Pattern Recognition Letters,2014,46(1):75-82.[
[3]]Li N,Hu H,Zhao Y,et al.A Spatial Clustering Method with Edge Weighting for Image Segmentation[J].IEEE Geoscience and Remote Sensing Letters,2013,10(5):1124-1128.[4]Yu A,Clausi D.IRGS:Image Segmentation Using Edge Penalties and Region Growing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(12):2126-2139.
[5]Yang X,Clausi D.Evaluating SAR Sea Ice Image Segmentation Using Edge-preserving Region-based MRFs[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2012,5(5):1383-1393.
[6]Liu Aiping,Fu Kun,You Hongjia.SAR Image Segmentation based on Multiscale Auto Regressive[J].Journal of Electronics and Information Technology,2009,31(11):2557-2562.[刘爱平,付琨,尤红建.基于MAR-MRF的SAR图像分割方法[J].电子与信息学报,2009,31(11):2557-2562.]
[7]Melas D,Wilson S.Double Markov Random Fields and Bayesian Image Segmentation[J].IEEE Transaction on Signal Process,2002,50(2):357- 365.
[8]Cao Lanying,Xia Liangzheng,Zhang Kunhui.SAR Image Segmentation Using MRF Model in Wavelet Domain[J].Journal of Southeast University (Natural Science Edition),2004,36(4):847-850.[曹兰英,夏良正,张昆辉.基于小波域MRF模型的SAR图像分割[J].东南大学学报(自然科学版),2004,36(4):847-850.]
[9]Wright J,Ganesh A,Rao S,et al.Robust Principal Component Analysis:Exact Recovery of Corrupted Low-rank Matrices Via Convex Optimization[C]//Proc Neural Information Processing Systems.British Columbia,Canada,2009,2080-2088.
[10]Xu Shengjun,Han Jiuqiang,He Bo.A Region Markov Random Field Model with Integrated Edge Feature and Image Segmentation Algorithm[J].Journal of Xi’an Jiaotong University,2014,48(2):14-19.[徐胜军,韩九强,何波.融合边缘特征的马尔可夫随机场模型及分割算法[J].西安交通大学学报,2014,48(2):14-19.]
[11]Song Xiaofeng,Wang Shuang,Liu Fang.SAR Image Segmentation Using Markov Random Field based on Regions and Bayes Belief Propagation[J].Acta Electronica Sinica,2010,38(12):2810-2815.[宋晓峰,王爽,刘芳.基于区域MRF 和贝叶斯置信传播的SAR 图像分割[J].电子学报,2010,38(12):2810-2815.]
[12]Yu Q Y,Clausi D A.IRGS:Image Segmentation Using Edge Penalties and Region Growing[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(12):2126 - 2139.
[13]Ding Rui,Liu Jiajia,Li Bolin.Modified Multilevel Thresholding Otsu Method for Image Segmentation[J].Journal of Computer Application,2013,33(Sup.1):214-217.[丁锐,刘甲甲,李柏林.改进的Otsu图像多阈值分割方法[J].计算机应用,2013,33(增刊1):214-217.]
[1] 李姣姣,刘玉,陈锟山. 基于香农熵的极化SAR相干矩阵信息量评价#br#[J]. 遥感技术与应用, 2018, 33(5): 842-849.
[2] 王常颖,田德政,韩园峰,隋毅,初佳兰. 基于属性差决策树的全极化SAR影像海冰分类[J]. 遥感技术与应用, 2018, 33(5): 975-982.
[3] 郭欣,赵银娣. 基于Sentinel-1A SAR的湖南省宁乡市洪水监测[J]. 遥感技术与应用, 2018, 33(4): 646-656.
[4] 张程,张红,王超. 基于PCDM香农熵的全极化SAR图像船舶目标检测方法[J]. 遥感技术与应用, 2018, 33(3): 499-507.
[5] 刘建歌,慕德俊. 基于SAR影像海冰动态特征的提取方法[J]. 遥感技术与应用, 2018, 33(1): 55-60.
[6] 张王菲,陈尔学,李增元,赵磊,姬永杰. 干涉、极化干涉SAR技术森林高度估测算法研究进展[J]. 遥感技术与应用, 2017, 32(6): 983-997.
[7] 周晓宇,陈富龙. 四川大熊猫栖息地PALSAR时序数据森林覆盖动态监测研究[J]. 遥感技术与应用, 2017, 32(6): 1100-1106.
[8] 扎西央宗,李林,卓玛,冯岩,李学东,白玛央宗. 西藏年楚河流域冰川变化监测方法研究[J]. 遥感技术与应用, 2017, 32(6): 1126-1131.
[9] 姜爱辉,刘国林,陈富龙. 基于PALSAR-1影像的汉函谷关遗迹变化检测研究[J]. 遥感技术与应用, 2017, 32(5): 787-793.
[10] 尤江彬,陈富龙. 西域都护府/且末古城数字地望考与长波段雷达次地表考古初探[J]. 遥感技术与应用, 2017, 32(5): 794-800.
[11] 王苏芸,孙中昶,郭华东,申维. 基于面向对象的东营市城乡建设用地信息提取[J]. 遥感技术与应用, 2017, 32(4): 780-786.
[12] 孙亚勇,黄诗峰,李纪人,李小涛,马建威,曲伟. Sentinel-1A SAR数据在缅甸伊洛瓦底江下游区洪水监测中的应用[J]. 遥感技术与应用, 2017, 32(2): 282-288.
[13] 王娜,李强子,赵龙才,王红岩,李德江,黄慧萍. 基于变异系数法的SAR船舶检测优化研究[J]. 遥感技术与应用, 2017, 32(2): 305-314.
[14] 肖瑶,赵萍,范泽琳,陈国旭. TerraSAR-X数据在淮南矿区沉陷监测中的应用[J]. 遥感技术与应用, 2017, 32(1): 95-103.
[15] 张康宇,王苏娟,郭乔影,李正泉,韩冰,王秀珍,黄敬峰. 基于长时间序列ASAR数据的近海风场反演[J]. 遥感技术与应用, 2016, 31(6): 1059-1068.