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纸质出版日期： 2018-5 ，
录用日期： 2017-12-7
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胡娇静, 张继贤, 许君一, 赵争. 2018. 指数型多尺度影像序列的C-V模型SAR岸线快速自动分割. 遥感学报, 22(3): 478–486
Hu J J, Zhang J X, Xu J Y and Zhao Z. 2018. Approach for rapid segmentation of coastline based on the C-V model using the exponential sequence of multi-scale SAR images. Journal of Remote Sensing, 22(3): 478–486
为提高SAR影像岸线自动分割的精度和效率，针对传统二进制(影像序列生成的金字塔步长底数为
a
=2)多尺度C-V模型对初始条件敏感、收敛速度低的问题，提出指数型(影像序列生成的底数为
a
≥1)多尺度影像序列生成方法，本方法将传统多尺度影像序列的生成方式的底数2量化为
a
≥1的任意数，并应用筛选因素进行自动地快速识别海岸线。从海岸线分割结果和所需时间方面与已有传统二进制C-V模型算法进行对比，实验表明本文算法在保证精度的条件下单次迭代逼近海岸线的计算量上小于传统即二进制多尺度C-V模型的单次迭代计算量，总迭代次数有所减少，时间效率有所提高，提高了岸线自动分割的精度和效率。
The coastline
which defines the boundary between the sea and land
is the benchmark for the division of national territory and the exclusive economic zones of oceans
and it is crucial to the maintenance of the rights to oceans. Extracting coastline information rapidly and accurately has become a popular issue in ocean research. However
detecting the coastline from SAR images is challenging because the information from land and sea can be mixed in a complex landform
and the reflection information from the sea area is changeable. The C-V model possesses strong detection sensitivity and anti-noise capability for coastline detection. However
the method involves complex iterations and large amounts of calculation. The speed of coastline detection is low for large-sized SAR images. Reducing the number of iterations and improving testing efficiency are the focus of this study
and the accuracy of the results are guaranteed under this premise. Given that the existing binary C-V model for coastline segmentation has low convergence speed and is highly sensitive to initial conditions
a novel approach using the exponential sequence of SAR images is proposed to improve the accuracy and efficiency of automatic coastline segmentation of SAR images. In this study
the image sequence generated by the traditional method is changed to an exponential sequence based on any bottom number greater than 1. The coastline is automatically detected based on predefined features from the generated image sequence. The main research techniques adopted are as follows. (1) The exponential multi-scale technology of the traditional C-V model that reduces image size and obtains a series of images under different spatial resolutions is introduced. (2) A low-pass filter is used to polish a small-scale image sequence
which is easy to form and has a relatively smooth boundary. (3) The exponential image sequence with different scales and polished degrees splits the coastline based on the C-V model of the level set one by one. In the division of the coastline
the high-spatial-resolution image inherits the boundary extracted by using low-spatial-resolution images in the high level and refines the coastline further through the C-V model. Coastline detection accuracy is ensured
and experimental results show that the computation effort of each iteration in the presented approach is less than that in the segmentation procedure based on the binary C-V model. The amount of iteration frequency is also reduced. The multi-scale method achieves image conversion from a low scale to a high scale. The low-pass filter removes small and pointless feature information
which improves the level set of the C-V model to achieve a simple iteration
significantly reduces time
and refines the boundary. The entire process ensures efficiency
high precision of edge extraction
and fast running time. Compared with the traditional binary C-V model
the presented approach is more efficient and effective in terms of computation effort and detection accuracy. Experimental results show that the presented method accelerates the acquisition of initial level set information and shortens the time of coastline extraction. The method also removes the non-coastline body and improves the detection precision of the main coastline body
thus ensuring a robust coastline detection process.
SAR指数型影像序列海岸线分割多尺度C-V模型
SARexponential image sequencecoastline segmentationmulti-scaleC-V model
Baselice F, Ferraioli G and Pascazio V. 2012. Coastal line extraction from SAR multi-channel images//Proceedings of 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing. Naples: IEEE, 2012: 322–325 [DOI: 10.1109/TyWRRS.2012.6381149]
Boak E H and Turner I L. 2005. Shoreline definition and detection: a review. Journal of Coastal Research, 21(4): 688–703
Brox T and Weickert J. 2004. Level set based image segmentation with multiple regions//Proceedings of the 26th DAGM Symposium. Tübingen, Germany: Springer: 415–423 [DOI: 10.1007/978-3-540-28649-3_51]
Cao G, Mao Z H, Yang X and Xia D S. 2008. Optical aerial image partitioning using level sets based on modified Chan-Vese mode. Pattern Recognition Letters, 29(4): 457–464
Chan T F and Vese L A. 2001. Active contours without edges. IEEE Transactions on Image Processing, 10(2): 266–277
Chen W W and Chang H K. 2009. Estimation of shoreline position and change from satellite images considering tidal variation. Estuarine, Coastal and Shelf Science, 84(1): 54–60
陈祥, 孙俊, 尹奎英, 于俊朋. 2014. 基于Otsu与海域统计特性的SAR图像海陆分割算法. 数据采集与处理, 29(4): 603–608
Chen X, Sun J, Yin K Y and Yu J P. 2014. Sea-land segmentation algorithm of SAR image based on Otsu method and statistical characteristic of sea area. Journal of Data Acquisition and Processing, 29(4): 603–608 (
Courant R, Friedrichs K and Lewy H. 1967. On the partial difference equations of mathematical physics. IBM Journal of Research and Development, 11(2): 215–234
Dellepiane S, De Laurentiis R and Giordano F. 2004. Coastline extraction from SAR images and a method for the evaluation of the coastline precision. Pattern Recognition Letters, 25(13): 1461–1470
邓少平, 张继贤, 李平湘. 2011. 极化SAR影像边缘检测综述. 计算机工程与应用, 47(22): 1–5, 16
Deng S P, Zhang J X and Li P X. 2011. Review of edge detection from polarimetric SAR imagery. Computer Engineering and Applications, 47(22): 1–5, 16 (
Dragotti P L, Vetterli M and Blu T. 2005. Exact sampling results for signals with finite rate of innovation using Strang-Fix conditions and local kernels//Proceedings of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, PA, USA: IEEE: 223–236 [DOI: 10.1109/ICASSP.2005.1415988]
冯文卿, 张永军. 2015. 利用多尺度融合进行面向对象的遥感影像变化检测. 测绘学报, 44(10): 1142–1151
Feng W Q and Zhang Y J. 2015. Object-oriented change detection for remote sensing images based on multi-scale fusion. Acta Geodaetica et Cartographica Sinica, 44(10): 1142–1151 (
Gao S and Bui T D. 2005. Image segmentation and selective smoothing by using Mumford-Shah model. IEEE Transactions on Image Processing, 14(10): 1537–1549
顾丹丹, 许小剑, 张秀玲. 2013. 一种基于小波变换和OTSU阈值的SAR图像海陆分割方法. 中国, CN102968798A
Gu D D, Xu X J and Zhang X L. 2013. SAR (Synthetic Aperture Radar) image sea-land segmentation method based on wavelet transform and OTSU threshold. CN, CN102968798A
Hai G S, Xu C, Liu J Y, Sun K M and Wen C F. 2012. A novel multi-scale level set method for SAR image segmentation based on a statistical model. International Journal of Remote Sensing, 33(17): 5600–5614
Heygster G, Dannenberg J and Notholt J. 2010. Topographic mapping of the german tidal flats analyzing SAR images with the waterline method. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1019–1030
胡娇静, 赵争, 王志勇, 王士帅. 2017. SAR影像海岸线提取方法对比及效果评价. 遥感信息, 32(4): 58–63
Hu J J, Zhao Z, Wang Z Y and Wang S S. 2017. Comparison and effectiveness evaluation for methods of coastline extraction from SAR imagery. Remote Sensing Information, 32(4): 58–63 (
寇光杰, 王贞松, 姚萍. 2011. SAR图像中不同特性条纹噪声的滤除. 遥感学报, 15(1): 1–12
Kou G J, Wang Z S and Yao P. 2011. Filtering methods for different characteristic stripe noise in SAR image. Journal of Remote Sensing, 15(1): 1–12 (
Lie J, Lysaker M and Tai X C. 2006. A binary level set model and some applications to Mumford-Shah image segmentation. IEEE Transactions on Image Processing, 15(5): 1171–1181
林政宗, 吕科, 杨磊, 徐文, 翟锐. 2014. 基于Level Set的海岸线检测. 电子学报, 42(2): 391–397
Lin Z Z, Lv K, Yang L, Xu W and Zhai R. 2014. A method for coastline detection based on level set. Acta Electronica Sinica, 42(2): 391–397 (
刘鹏程. 2015. 基于水平集理论的海岸线轮廓特征提取. 国土资源遥感, 27(2): 75–79
Liu P C. 2015. Feature extraction of coastline contour based on level set theory. Remote Sensing for Land and Resources, 27(2): 75–79 (
Mason D C and Davenport I J. 1996. Accurate and efficient determination of the shoreline in ERS-1 SAR images. IEEE Transactions on Geoscience and Remote Sensing, 34(5): 1243–1253
Ogawa H. 1986. A unified approach to generalized sampling theorems//Proceedings of 1986 IEEE International Conference on Acoustics, Speech, and Signal Processing. Tokyo, Japan: IEEE: 1657–1660 [DOI: 10.1109/ICASSP.1986.1168936]
Osher S and Fedkiw R. 2002. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag: 3–124
欧阳越, 种劲松. 2004. 基于改进水平截集算法的SAR图像海岸线检测. 遥感技术与应用, 19(6): 456–460
Ouyang Y and Zhong J S. 2004. Coastline detection method in SAR images based on an improved level set algorithm. Remote Sensing Technology and Application, 19(6): 456–460 (
Sonka M, Hlavac V and Boyle R. 2008. Image Processing, Analysis, and Machine Vision. 3rd ed. Australia: Thomson Learning: 275–282
陶珂, 朱建军. 2012. 多指标融合的小波去噪最佳分解尺度选择方法. 测绘学报, 41(5): 749–755
Tao K and Zhu J J. 2012. A hybrid indicator for determining the best decomposition scale of wavelet Denoising. Acta Geodaetica et Cartographica Sinica, 41(5): 749–755 (
王大凯, 侯榆青, 彭进业. 2008. 图像处理的偏微分方程方法. 北京: 科学出版社: 1–121
Wang D K, Hou Y Q and Peng J Y. 2008. Partial Differential Equation Method for Image Processing. Beijing: Science Press: 1–121
吴诗婳, 吴一全, 周建江, 孟天亮. 2015. 利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割. 测绘学报, 44(11): 1255–1262
Wu S H, Wu Y Q, Zhou J J and Meng T L. 2015. SAR river image segmentation based on reciprocal gray entropy and improved Chan-Vese model. Acta Geodatetica et Cartographica Sinica, 44(11): 1255–1262 (
谢明鸿, 张亚飞, 付琨. 2007. 基于种子点增长的SAR图像海岸线自动提取算法. 中国科学院研究生学报, 24(1): 93–98
Xie M H, Zhang Y F and Fu K. 2007. Algorithm of detection coastline from SAR images based on seeds growing. Journal of the Graduate School of the Chinese Academy of Science, 24(1): 93–98 (
徐川, 华凤, 眭海刚, 陈光. 2014. 多尺度水平集SAR影像水体自动分割方法. 武汉大学学报(信息科学版), 39(1): 27–31
Xu C, Hua F, Sui H G and Chen G. 2014. Automatic water segmentation method in SAR images using Muti-scale Level Set. Geomatics and Information Science of Wuhan University, 39(1): 27–31 (
于彩霞, 王家耀, 许军, 彭认灿, 成毅, 王沫. 2014. 海岸线提取技术研究进展. 测绘科学技术学报, 31(3): 305–309
Yu C X, Wang J Y, Xu J, Peng R C, Cheng Y and Wang M. 2014. Advance of coastline extraction technology. Journal of Geomatics Science and Technology, 31(3): 305–309 (
Zhang K H, Zhang L, Song H H and Zhou W G. 2010. Active contours with selective local or global segmentation: a new formulation and level set method. Images and Vision Computing, 28(4): 668–676
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