|Table of Contents|

A New Algorithm Based on Hybrid Feature Space MRF(MarkovRandom Filed)Model for Water Information Extractionfrom High Resolution Remote Sensing Imagery(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
13-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
A New Algorithm Based on Hybrid Feature Space MRF(MarkovRandom Filed)Model for Water Information Extractionfrom High Resolution Remote Sensing Imagery
Author(s):
Li ShijinWang Shengte
College of Computer and Information,Hohai University,Nanjing 210098,China
Keywords:
water body extractionMRF modelnormalized difference water index(NDWI)hybrid feature spaceGraph Cut
PACS:
TP753
DOI:
10.3969/j.issn.1001-4616.2017.01.003
Abstract:
Water information extraction in remote sensing images is an important application of remote sensing technology in water resources surveying and utilization,detection of water ecology change and other aspects. The existing water extraction methods as water index or image classification are not accurate enough for water boundary treatment,and they are easy to produce the problem of error extraction and leakage extraction. Based on the existing algorithms that constructing water index to extract water information,we have proposed a new algorithm which combine image segmentation algorithm based on MRF model with normalized difference water index(NDWI)for extraction of water information. We represent the pixels in a remote sensing image as random variables in an MRF model,and introduce hybrid feature in the energy function on these variables,minimize the energy function to find the optimal water boundary,by using an iterative graph cut scheme. Then water boundary is adaptively refined according to the water index and color feature of the extracted water body. The experiment of water information extraction in Shilianghe Reservoir shows that our approach can achieve significant accuracy as it automatically adapts to the extraction of water information in reservoir whose surroundings are complicated and the boundary of water bodies is handled precisely.

References:

[1] 胡晓东,骆剑承,夏列钢,等. 图谱迭代反馈的自适应水体信息提取方法[J]. 测绘学报,2011,40(5):544-550.
[2]骆剑承,盛永伟,沈占锋,等. 分步迭代的多光谱遥感水体信息高精度自动提取[J]. 遥感学报,2009,13(4):610-615.
[3]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.
[4]王华. 基于 VWRD 的遥感影像面状居民地和水体提取[D]. 武汉:武汉大学,2010.
[5]FRAZIER P S,PAGE K J. Water body detection and delineation with Landsat TM data[J]. Photogrammetric engineering and remote sensing,2000,66(12):1 461-1 468.
[6]MCFEETERS S K. The use of normalized difference water index(NDWI)in the delineation of open water features[J]. International journal of remote sensing,1996,17(7):1 425-1 432.
[7]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.
[8]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.
[9]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):1 166-1 172.
[10]吉红霞,范兴旺,吴桂平,等. 离散型湖泊水体提取方法精度对比分析[J]. 湖泊科学,2015,27(2):327-334.
[11]陈亮,张友静,何厚军,等. 基于混合像元分解的水体面积提取算法[J]. 河海大学学报(自然科学版),2014,42(4):346-350.
[12]BOYKOV Y Y,JOLLY M P. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images[C]//2001 IEEE International Conference on Computer Vision(ICCV). Vancouver,Canada:IEEE,2001:105-112.
[13]朱利,李云梅,赵少华,等. 基于GF-1号卫星WFV数据的太湖水质遥感监测[J]. 国土资源遥感,2015,27(1):113-120.
[14]MISHRA A,ALAHARI K,JAWAHAR C V. An MRF model for binarization of natural scene text[C]//2011 International Conference on Document Analysis and Recognition(ICDAR). Beijing,China:IEEE,2011:11-16.
[15]刘磊,石志国,宿浩茹,等. 基于高阶马尔可夫随机场的图像分割[J]. 计算机研究与发展,2013,50(9):1 933-1 942.
[16]ROTHER C,KOLMOGOROV V,BLAKE A. Grabcut:interactive foreground extraction using iterated graph cuts[J]. ACM transactions on graphics(TOG),2004,23(3):309-314.
[17]BOYKOV Y,KOLMOGOROV V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J]. IEEE transactions on pattern analysis and machine intelligence,2004,26(9):1 124-1 137.

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