SAR图像河流提取的主动轮廓模型的稳健估计算法

doi:10.11947/j.AGCS.2020.20180423

测绘学报 ›› 2020, Vol. 49 ›› Issue (6): 777-786.doi: 10.11947/j.AGCS.2020.20180423

SAR图像河流提取的主动轮廓模型的稳健估计算法

韩斌, 吴一全

南京航空航天大学电子信息工程学院, 江苏南京 211106

收稿日期:2018-09-11 修回日期:2020-02-11 出版日期:2020-06-20 发布日期:2020-06-28
通讯作者: 吴一全 E-mail:nuaaimage@163.com
作者简介:韩斌(1990-),男,博士生,研究方向为遥感图像处理。E-mail:909907566@qq.com
基金资助:
国家自然科学基金（61573183）

Robust estimation algorithm of active contour model for river extraction in SAR images

HAN Bin, WU Yiquan

College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2018-09-11 Revised:2020-02-11 Online:2020-06-20 Published:2020-06-28
Supported by:
The National Natural Science Foundation of China (No. 61573183)

摘要/Abstract

摘要： 针对现有主动轮廓模型无法精确提取SAR图像中河流的难题，提出了一种结合L₁范数和拉普拉斯能量的主动轮廓模型。首先，将Chan-Vese（CV）模型中L₂范数形式的外部能量约束项替换为L₁范数形式的外部能量约束项，得到新的能量泛函；其次，提出了一种基于拉普拉斯核函数的外部能量约束项，并将其添加到上述能量泛函中，同时赋予两种外部能量约束项不同的调节系数；最后，引入曲线内外区域像素灰度绝对中位差的均值替代模型中的常数曲线内外能量权值，以得到完整的提出模型。针对实际SAR图像进行河流提取，结果表明：与现有主动轮廓模型相比，本文提出的模型在河流提取准确性和提取效率两方面具有明显优势。

关键词: SAR图像, 河流提取, 主动轮廓模型, L₁范数, 拉普拉斯核函数, 绝对中位差

Abstract: To deal with the problem that the existing active contour models are unable to extract rivers in SAR images accurately, this paper presents an new active contour model with L₁ norm and Laplacian energies. First, the external energy constraint in form of the L₂ norm in the CV model is replaced by the external energy constraint in form of the L₁ norm and then the novel energy functional is obtained. Second, an external energy constraint based on the Laplacian kernel function is proposed and added to the above energy functional. Meanwhile, the different adjustment coefficients are assigned to these two external energy constraints. Finally, the mean of median absolute deviations of pixel grayscale values inside and outside the curve is introduced to replace the constant energy weights inside and outside the curve of the model and then the completed proposed model is developed. River extraction is carried out on real SAR images and the results reveal the superiority of the proposed model on both accuracy and efficiency of the river extraction, when compared with the existing active contour models.

Key words: SAR image, river extraction, active contour model, L₁ norm, Laplacian kernel function, absolute median deviation

中图分类号:

P237

韩斌, 吴一全. SAR图像河流提取的主动轮廓模型的稳健估计算法[J]. 测绘学报, 2020, 49(6): 777-786.

HAN Bin, WU Yiquan. Robust estimation algorithm of active contour model for river extraction in SAR images[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 777-786.

参考文献 25

[1]	吴一全, 孟天亮, 吴诗婳, 等. 基于二维倒数灰度熵的河流遥感图像分割[J]. 华中科技大学学报(自然科学版), 2014, 42(12):70-74, 80. WU Yiquan, MENG Tianliang, WU Shihua, et al. Remote sensing images segmentation of rivers based on two-dimensional reciprocal gray entropy[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, 42(12):70-74, 80.
[2]	慎利, 唐宏, 王世东, 等. 结合空间像素模板和Adaboost算法的高分辨率遥感影像河流提取[J]. 测绘学报, 2013, 42(3):344-350. SHEN Li, TANG Hong, WANG Shidong, et al. River extraction from the high-resolution remote sensing image based on spatially correlated pixels template and Adaboost algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3):344-350.
[3]	吴一全, 李海杰, 宋昱. 基于多特征和WSVM的SAR图像河流目标检测[J]. 系统工程与电子技术, 2015, 37(6):1288-1293. WU Yiquan, LI Haijie, SONG Yu. Target detection algorithm for rivers in SAR images based on multi-features and WSVM[J]. Systems Engineering and Electronics, 2015, 37(6):1288-1293.
[4]	BLAIN C A, LINZELL R, MCKAY P. Simple methodology for deriving continuous shorelines from imagery:application to rivers[J]. Journal of Waterway, Port, Coastal and Ocean Engineering, 2013, 139(5):365-382.
[5]	ZHAO Jianjun, YU Hheng, LI Helin, et al. The edge extraction of yellow river model based on the principle of SUSAN[C]//Proceedings of the 2nd IEEE International Conference on Computing, Control and Industrial Engineering. Wuhan, China:IEEE, 2011:288-291.
[6]	SUN Jinping, MAO Shiyi. River detection algorithm in SAR images based on edge extraction and ridge tracing techniques[J]. International Journal of Remote Sensing, 2011, 32(12):3485-3494.
[7]	BADURA P, WIECLAWEK W. Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation[J]. Applied Soft Computing, 2016, 49:887-900.
[8]	MONDAL A, GHOSH S, GHOSH A. Robust global and local fuzzy energy based active contour for image segmentation[J]. Applied Soft Computing, 2016, 47:191-215.
[9]	LI Qiang, DENG Tingquan, XIE Wei. Active contours driven by divergence of gradient vector flow[J]. Signal Processing, 2016, 120:185-199.
[10]	LIU Weiping, SHANG Yanfeng, YANG Xin. Active contour model driven by local histogram fitting energy[J]. Pattern Recognition Letters, 2013, 34(6):655-662.
[11]	ZHOU Sanping, WANG Jinjun, ZHANG S, et al. Active contour model based on local and global intensity information for medical image segmentation[J]. Neurocomputing, 2016, 186:107-118.
[12]	WANG Hui, HUANG Tingzhu, XU Zongben, et al. An active contour model and its algorithms with local and global Gaussian distribution fitting energies[J]. Information Sciences, 2014, 263:43-59.
[13]	WANG Hui, HUANG Tingzhu, XU Zhi, et al. A two-stage image segmentation via global and local region active contours[J]. Neurocomputing, 2016, 205:130-140.
[14]	ZHOU Yan, SHI Weiren, CHEN Wei, et al. Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images[J]. Neurocomputing, 2015, 156:199-210.
[15]	WU Yue, MA Wenping, GONG Maoguo, et al. Novel fuzzy active contour model with kernel metric for image segmentation[J]. Applied Soft Computing, 2015, 34:301-311.
[16]	JIANG Xiaoliang, WANG Qiang, HE Biao, et al. Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints[J]. Neurocomputing, 2016, 207:22-35.
[17]	吴诗婳, 吴一全, 周建江, 等. 利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割[J]. 测绘学报, 2015, 44(11):1255-1262. DOI:10.11947/j.AGCS.2015.20140519. WU Shihua, WU Yiquan, ZHOU Jianjiang, et al. SAR river image segmentation based on reciprocal gray entropy and improved Chan-Vese model[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11):1255-1262. DOI:10.11947/j.AGCS.2015.20140519.
[18]	SONG Yu, WU Yiquan, DAI Yimian. A new active contour remote sensing river image segmentation algorithm inspired from the cross entropy[J]. Digital Signal Processing, 2016, 48:322-332.
[19]	HAN Bin, WU Yiquan. A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation[J]. Pattern Recognition, 2017, 67:396-409.
[20]	CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2):266-277.
[21]	LI Chunming, XU Chenyang, GUI Changfeng, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254.
[22]	HAN Bin, WU Yiquan. Active contours driven by global and local weighted signed pressure force for image segmentation[J]. Pattern Recognition, 2019, 88:715-728.
[23]	HAN Bin, WU Yiquan. A hybrid active contour model driven by novel global and local fitting energies for image segmentation[J]. Multimedia Tools and Applications, 2018, 77(21):29193-29208.
[24]	HAN Bin, WU Yiquan. River extraction of SAR images via active contours driven by adaptive global fitting energies[J]. International Journal of Remote Sensing, 2018, 39(20):6995-7013.
[25]	RODTOOK A, KIRIMASTHONG K, LOHITVISATE W, et al. Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities[J]. Pattern Recognition, 2018, 79:172-182.

SAR图像河流提取的主动轮廓模型的稳健估计算法

Robust estimation algorithm of active contour model for river extraction in SAR images

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