Improved ACM Algorithm for Poyang Lake Monitoring

LENG Ying; LIU Zhongling; ZHANG Heng; WANG Yu; LI Ning

doi:10.11999/JEIT160870

Volume 39 Issue 5

May 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(5): 1064-1070

LENG Ying, LIU Zhongling, ZHANG Heng, WANG Yu, LI Ning. Improved ACM Algorithm for Poyang Lake Monitoring[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1064-1070. doi: 10.11999/JEIT160870

Citation:

LENG Ying, LIU Zhongling, ZHANG Heng, WANG Yu, LI Ning. Improved ACM Algorithm for Poyang Lake Monitoring[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1064-1070. doi: 10.11999/JEIT160870

Citation:

PDF( 9942 KB)

Improved ACM Algorithm for Poyang Lake Monitoring

doi: 10.11999/JEIT160870

1.
2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Natural Science Fund of China for Excellent Young Scholars (61422113)

Received Date: 2016-08-24
Rev Recd Date: 2017-01-20
Publish Date: 2017-05-19

Abstract

Abstract

Sentinel-1 satellite constellation offers enough Synthetic Aperture Radar (SAR) images for long-term water monitoring, due to its relative large swath, great revisit frequency and high resolution. The middle and upper Yangtze River suffers serious flood disaster in 2016. It is significant to detect water and its changes of Poyang Lake, since it is one of the important flood storage lakes along the Yangtze River mainstream. However, the traditional segmentation algorithm has shortage in edge preservation and the accuracy of water detection, especially in the case of Poyang Lake, which is widely distributed and has more complex background, weak edges and blurred edges. A new Active Contour Model (ACM) algorithm based on local narrowband is proposed to solve these problems, and it is applied to Sentinel-1A observations related to Poyang Lake. First, a cascade two-level Otsu approach is adopted to obtain the initial contour. Second, the local narrowband is built along the initial contour to reduce the calculating time. Finally, a region-based ACM is introduced into the local narrowband to stop the contours at weak or blurred edges. Experiment results show that the new method has advantages in the edge preservation and obtains better segmentation results with respect to other methods.
- Synthetic Aperture Radar (SAR),
- Poyang lake,
- Local narrowband,
- Water detection

FullText(HTML)

References(19)

References

YESOU H, HUBER C, HAOUET S, et al. Exploiting sentinel 1 time series to monitor the largest fresh water bodies in PR China, the Poyang lake[C]. IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 3882-3885. doi: 10.1109/IGARSS.2016.7730008.

ZHANG P, FENG L, LU J Z, et al. Hydrodynamic and inundation modeling of Chinas largest freshwater lake aided by remote sensing data[J]. Remote Sensing, 2015, 7(4): 4858-4879. doi: 10.3390/rs70404858.

LAI X J, SHANKMAN D, HUBER C, et al. Sand mining and increasing Poyang Lakes discharge ability: A reassessment of causes for lake decline in China[J]. Journal of Hydrology, 2014, 519(1): 1698-1706. doi: 10.1016/j.jhydrol.2014.09.058.

YE X C, ZHANG Q, LIU J, et al. Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China[J]. Journal of Hydrology, 2013, 494(12): 83-95. doi: 10.1016 /j.jhydrol.2013.04.036.

FENG L, HU C, CHEN X, et al. Dramatic inundation changes of Chinas two largest freshwater lakes linked to the Three Gorges Dam[J]. Environmental Science and Technology, 2013, 47(17): 9628-9634. doi: 10.1021/es4009618.

FENG L, HU C M, HAN X X, et al. Long-term distribution patterns of Chlorophyll-a concentration in Chinas largest freshwater lake: MERIS full-resolution observations with a practical approach[J]. Remote Sensing, 2015, 7(1): 275-299. doi: 10.3390/rs70100275.

LI L, XIA H, LI Z, et al. Temporal-spatial evolution analysis of lake size-distribution in the middle and lower Yangtze river basin using Landsat imagery data[J]. Remote Sensing, 2015, 7(8): 10364-10384. doi: 10.3390/rs70810364.

安成锦, 牛照东, 李志军, 等. 典型 Otsu 算法阈值比较及其SAR 图像水域分割性能分析[J]. 电子与信息学报, 2010, 32(9): 2215-2219. doi: 10.3724/SP.J.1146.2009.01426.

AN Chengjin, NIU Zhaodong, LI Zhijun, et al. Otsu threshold comparison and SAR water segmentation result analysis[J]. Journal of Electronics Information Technology, 2010, 32(9): 2215-2219. doi: 10.3724/SP.J.1146.2009.01426.

SHENG G F, YANG W, DENG X P, et al. Coastline detection in synthetic aperture radar (SAR) Images by integrating watershed transformation and controllable gradient vector flow (GVF) snake model[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3): 375-383. doi: 10.1109/JOE. 2012.2191998.

颜学颖, 焦李成, 王凌霞, 等. 一种提高SAR 图像分割性能的新方法[J]. 电子与信息学报, 2011, 33(7): 1700-1705. doi: 10.3724/SP.J.1146.2010.01190.

YAN Xueying, JIAO Licheng, WANG Lingxia, et al. New method for improving the performance of SAR image segmentation[J]. Journal of Electronics Information Technology, 2011, 33(7): 1700-1705. doi: 10.3724/SP.J.1146. 2010.01190.

CASELLES V, KIMMEL R, and SAPIRO G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22(1): 61-79. doi: 10.1023/A:1007979827043.

ADALSTEINSSON D and SETHIAN J A. A fast level set method for propagating interfaces[J]. Journal of Computational Physics, 1995, 118(2): 269-277. doi: 10.1006/ jcph.1995.1098.

ZHANG K, ZHANG L, SONG H, et al. Active contours with selective local or global segmentation: A new formulation and level set method[J]. Image Vision Computing, 2010, 28(4): 668-676. doi: 10.1016/j.imavis.2009.10.009.

XU C, YEZZI A, and PRINCE J L. On the relationship between parametric and geometric active contours[C]. IEEE Signals, Systems and Computers, Asilomar, USA, 2000: 483-489. doi: 10.1109/ACSSC.2000.911003.

LIU Z L, LI N, WANG R, et al. A novel region-merging approach for coastline extraction from Sentinel-1A IW mode SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 324-328. doi: 10.1109/LGRS.2015. 2510745.

BASELICE F and FERRAIOLI G. Unsupervised coastal line extraction from SAR images[J]. IEEE Geoscience Remote Sensing Letters, 2013, 10(6): 1350-1354. doi: 10.1109/LGRS. 2013.2241013.

SHU Y M, LI J, and YOUSIF H. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9): 2026-2035. doi: 10.1016/j.rse.2010.04.009.

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(8)

1.	包立男，吕孝雷. 基于改进的高斯混合模型和图割模型的水体图像提取算法. 中国科学院大学学报(中英文). 2024(06): 794-802 .
2.	王远明，宫克，毛飞雄，杨明思，肖龙，李明辉. 基于ACM及机器学习的腐蚀监测分析系统在重大基础设施上的应用. 中国表面工程. 2024(06): 205-215 .
3.	李宁，郭志顺，毋琳，赵建辉. River-Net：面向河道提取的Refined-Lee Kernel深度神经网络模型. 雷达学报. 2022(03): 324-334 .
4.	陈嘉琪，刘祥梅，李宁，张燕. 一种超分辨SAR图像水域分割算法及其应用. 电子与信息学报. 2021(03): 700-707 . 本站查看
5.	李宁，牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法. 雷达学报. 2020(01): 174-184 .
6.	闵林，王宁，毋琳，李宁，赵建辉. 基于多源雷达遥感技术的黄河径流反演研究. 电子与信息学报. 2020(07): 1590-1598 . 本站查看
7.	汤玲英，刘雯，杨东，陈乐，苏扬媚，徐宪立. 基于面向对象方法的Sentinel-1A SAR在洪水监测中的应用. 地球信息科学学报. 2018(03): 377-384 .
8.	牛世林，郭拯危，李宁，毋琳，赵建辉. 星载SAR水域分割研究进展与趋势分析. 聊城大学学报(自然科学版). 2018(02): 72-86 .