Please wait a minute...
Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 129-137     DOI: 10.6046/gtzyyg.2020067
The extraction of plateau lakes from SAR images based on Faster R-CNN and MorphACWE model
DONG Tiancheng1(), YANG Xiao1, LI Hui2, ZHANG Zhi1(), QI Rui3
1. Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China
2. School of Earth Science,China University of Geosciences, Wuhan 430074, China
3. 32023 Troops, Dalian 116032, China
Download: PDF(5321 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Lakes in the Tibetan Plateau constitute one of the most important natural factors in the plateau ecological environment. So, it is an urgent task to investigate and monitor lakes in the Tibetan Plateau. Because of the unique backscatter characteristics of water body in the image, the extraction and analysis of the lake based on SAR image has become a research hotspot. In order to further eliminate the interference of surface features and improve the classification accuracy, this paper proposes a high-precision lake extraction FR-MorphACWE (Faster Region-based Convolution Neural Network-MorphACWE) model of SAR image. The Interferometric Wide Swath (IW SLC) of the European Space Agency's sentinel-1A interference wide-band mode is used as the main data source, and the sentinel-2a multispectral image level-1c product is used as the reference data source. This model combines the morphological analysis advantages of Faster R-CNN target detection algorithm and the contour extraction advantages of MorphACWE model. The classification experiments were carried out from extraction of comprehensive interference multi-lake. The target detection algorithm was applied to eliminate non - lake surface disturbance. On such a basis, the active contour model was used to extract the lake boundary, and the morphological characteristics and radar reflection characteristics of plateau lakes were fully utilized to achieve high-precision extraction of plateau lakes from the south of Naqu City to the north of Xigaze City in Tibet. The experimental results show that the accuracy of the algorithm can reach 99.71% and the accuracy and recall rate are higher than 98% in the situation of multi-lake interference.

Keywords Target detection      Faster R-CNN      CV model      SAR      plateau lake extraction     
ZTFLH:  TP79  
Corresponding Authors: ZHANG Zhi     E-mail:;
Issue Date: 18 March 2021
E-mail this article
E-mail Alert
Articles by authors
Tiancheng DONG
Hui LI
Rui QI
Cite this article:   
Tiancheng DONG,Xiao YANG,Hui LI, et al. The extraction of plateau lakes from SAR images based on Faster R-CNN and MorphACWE model[J]. Remote Sensing for Land & Resources, 2021, 33(1): 129-137.
URL:     OR
Fig.1  Research area overview
数据类型 成像时间 极化方式 空间分
Sentinel-1A 2019-07-01 VV 5×20 250×180
2019-07-08 VV
2019-07-13 VV
2019-07-25 VV/VH
2019-07-27 VV/VH
2019-07-31 VV
2019-08-01 VV
2019-08-06 VV
2019-08-07 VV/VH
2019-08-16 VV
Sentinel-2A 2019-07-22 10 110×110
Tab.1  Data in the study area
Fig.2  Structure of Faster R-CNN
Fig.3  Structure of Improved VGG16
Fig.4  Structure of FR-MorphACWE
Fig.5  Experimental procedures
参数 基础学
动量 子训
取值 0.001 0.9 256 0.7 40 000 700
Tab.2  FR-MorphACWE super parameter settings
Fig.6  Loss rate line chart
Fig.7  Multi-lake SAR image intensity value comparison
Tab.3  Comparison of extraction results of different classification methods
分类方法 准确率/% 精准率/% 召回率/% F1分数 Kappa系数
OTSU阈值分割 94.612 2 92.518 4 69.244 7 79.207 3 0.761 9
模糊C均值算法 85.215 2 96.671 9 42.655 4 59.192 7 0.517 7
Mask R-CNN算法 98.791 0 94.686 3 94.429 4 94.557 7 0.938 8
FR-Morph-ACWE算法 99.716 4 98.809 7 98.636 4 98.723 0 0.985 6
Tab.4  Accuracy comparison of different classification methods
Fig.8  Difference between the results of each classification method and the true value
[1] 王苏民, 窦鸿身, 陈克造, 等. 中国湖泊志[M]. 北京: 科学出版社, 1998: 7.
[1] Wang S M, Dou H S, Chen K Z, et al. China lakes[M]. Beijing: Science Press, 1998: 7.
[2] 崔雪芹. 中国科学家发起“第三极水塔计划”.[EB/OL](2019-07-13)
[2] Cui X Q. Chinese scientists launched the third pole water tower project.[EB/OL](2019-07-13)
[3] 卢善龙, 肖高怀, 贾立, 等. 2000—2012年青藏高原湖泊水面时空过程数据集遥感提取[J]. 国土资源遥感, 2016,28(3):181-187.doi: 10.6046/gtzyyg.2016.03.28.
doi: 10.6046/gtzyyg.2016.03.28 url:
[3] Lu S L, Xiao G H, Jia L, et al. Extraction of the spatial-temporal lake water surface dataset in the Tibetan Plateau over the past 10 years[J]. Remote Sensing for Land and Resources, 2016,28(3):181-187.doi: 10.6046/gtzyyg.2016.03.28.
[4] 李德仁, 周月琴, 马洪超. 卫星雷达干涉测量原理与应用[J]. 测绘科学, 2000,25(1):9.
[4] Li D R, Zhou Y Q, Ma H C. Principles and applications of interferometry SAR[J]. Science of Surveying and Mapping, 2000,25(1):9.
[5] 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 url:
[5] An C J, Niu Z D, Li Z J, et al. Otsu threshold comparison and SAR water segmentation result analysis[J]. Journal of Electronics & Information Technology, 2010,32(9):2215-2219
[6] 李智慧, 陈秀万, 罗鹏, 等. 基于混合阈值法的Envisat影像水体提取[J]. 遥感信息, 2013,28(2):62-65.
[6] Li Z H, Chen X W, Luo P, et al. A hybird thresholding method for water extraction in Envisat image[J]. Remote Sensing Information, 2013,28(2):62-65.
[7] Hahmann T, Wessel B. Surface water body detection in high-resolution TerraSAR-X data using active contour models[C]// Synthetic Aperture Radar (EUSAR),2010 8th European Conference on.VDE, 2010.
[8] 王庆, 廖静娟. 基于SAR数据的鄱阳湖水体提取及变化监测研究[J]. 国土资源遥感, 2010(4):94-100.doi: 10.6046/gtzyyg.2010.04.19.
[8] Wang Q, Liao J J. Water area extraction and change detection of the Poyang Lake using SAR data[J]. Remote Sensing for Land and Resources, 2010(4):94-100.doi: 10.6046/gtzyyg.2010.04.19.
[9] Jin H, Mountrakis G, Stehman S V. Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2014,98:70-84.
[10] 邓滢, 张红, 王超, 等. 结合纹理与极化分解的面向对象极化SAR水体提取方法[J]. 遥感技术与应用, 2016,31(4):714-723.
[10] Deng Y, Zhang H, Wang C, et al. An object-oriented water extraction method based on texture and polarimetric decomposition feature[J]. Remote Sensing Technology and Application, 2016,31(4):714-723.
[11] 冷英, 李宁. 一种改进的变化检测方法及其在洪水监测中的应用[J]. 雷达学报, 2017,6(2):204-212.
[11] Leng Y, Li N. Improved change detection method for flood monitoring[J]. Journal of Radars, 2017,6(2):204-212.
[12] Sghaier M O, Foucher S, Lepage R. River extraction from high-resolution SAR images combining a structural feature set and mathematical morphology[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016: 1-14.
[13] Li N, Niu S, Guo Z, et al. Dynamic waterline mapping of inland great lakes using time-series SAR data from GF-3 and S-1A satellites:A case study of DJK reservoir,China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019,12(11):4297-4314.
[14] Zhang Y, Chen J Q, Liu X, et al. Efficent extraction of Daihai Lake based on neural network algorithrn from high resolution SAR Image.2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama),AUG 1-4,2018[C]. Toyama,JAPAN.
[15] Shang F, Hirose A. Quaternion neural-network-based PolSAR land classification in poincare-sphere-parameter space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(9):5693-5703.
[16] Yang F Y, Feng T, Xu G Y, et al. Applied method for water-body segmentation based on mask R-CNN[J]. Journal of Applied Remote Sensing, 2020,14(1), 014502.
[17] Kaiming H, Georgia G, Piotr D, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018: 1.
[18] Ren S Q, He K M, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015,39(6):1137-1149.
doi: 10.1109/TPAMI.2016.2577031 pmid: 27295650 url:
[19] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab:Semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4):834.
pmid: 28463186 url:
[20] Chan T F, Vese L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001,10(2):266-277.
doi: 10.1109/83.902291 pmid: 18249617 url:
[21] Marquez-Neila P, Baumela L, Alvarez L. A Morphological approach to curvature-based evolution of curves and surfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,36(1):2-17.
doi: 10.1109/TPAMI.2013.106 pmid: 24231862 url:
[22] 张迎春, 郭禾. 基于粗糙集和新能量公式的水平集图像分割[J]. 自动化学报, 2015,41(11):1913-1925.
[22] Zhang Y C, Guo H. Level set image segmentation based on rough set and new energy formula[J]. Acta Automatica Sinica, 2015,41(11):1913-1925.
[23] 谢东, 龚劬, 陈小彪. 基于KFCM改进CV模型的SplitBregman图像分割方法[J]. 计算机科学, 2014(s1):153-155.
[23] Xie D, Gong Q, Chen X B. A Split Bregman image segmentation method based on KFCM and improved CV model[J]. Computer Science, 2014(s1):153-155.
[24] 李震, 郭华东, 李新武, 等, SAR干涉测量的相干性特征分析及积雪划分[J]. 遥感学报, 2002,6(5):334-338.
[24] Li Z, Guo H D, Li X W, et al. SAR Interferometry coherence analysis and snow mapping[J]. Journal of Remote Sensing, 2002,6(5):334-338.
[25] 马威, 陈登魁, 杨娜, 等. 时序双极化SAR开采沉陷区土壤水分估计[J]. 遥感学报, 2018,22(3):521-534.
[25] Ma W, Chen D K, Yang N, et al. Time-series approach to estimate the soil moisture of a subsidence area by using dual polarimetric radar data[J]. Journal of Remote Sensing, 2018,22(3):521-534.
[1] JIANG Decai, ZHENG Xiangxiang, WANG Ning, XIAO Chunlei, ZHU Zhenzhou. Application of the time-series InSAR technology in the identification of geological hazards in the Pearl River Delta region[J]. Remote Sensing for Natural Resources, 2023, 35(3): 292-301.
[2] DONG Ting, FU Weiqi, SHAO Pan, GAO Lipeng, WU Changdong. Detection of changes in SAR images based on an improved fully-connected conditional random field[J]. Remote Sensing for Natural Resources, 2023, 35(3): 134-144.
[3] LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
[4] GAO Chen, MA Dong, QU Man, QIAN Jianguo, YIN Haiquan, HOU Xiaozhen. Exploring the anomaly mechanism of borehole strain at the Huailai seismic station based on PS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 153-159.
[5] LIU Hui, XU Xinyue, CHEN Mi, CHEN Fulong, DING Ruili, LIU Fei. Time-series InSAR-based dynamic remote sensing monitoring of the Great Wall of the Ming Dynasty in Qinhuangdao[J]. Remote Sensing for Natural Resources, 2023, 35(2): 202-211.
[6] PAN Jianping, FU Zhanbao, DENG Fujiang, CAI Zhuoyan, ZHAO Ruiqi, CUI Wei. A time-series InSAR-based analysis of surface deformation of hydro-fluctuation belts and the effects of hydrological elements[J]. Remote Sensing for Natural Resources, 2023, 35(2): 212-219.
[7] QIU Lei, ZHANG Xuezhi, HAO Dawei. VideoSAR moving target detection and tracking algorithm based on deep learning[J]. Remote Sensing for Natural Resources, 2023, 35(2): 157-166.
[8] HU Xiaoqiang, YANG Shuwen, YAN Heng, XUE Qing, ZHANG Naixin. Time-series InSAR-based monitoring and analysis of surface deformation in the Axi mining area, Xinjiang[J]. Remote Sensing for Natural Resources, 2023, 35(1): 171-179.
[9] SUN Sheng, MENG Zhimin, HU Zhongwen, YU Xu. Application of multi-scale and lightweight CNN in SAR image-based surface feature classification[J]. Remote Sensing for Natural Resources, 2023, 35(1): 27-34.
[10] FU Yukai, YANG Shuwen, YAN Heng, XUE Qing, HONG Weili, SU Hang. An SAR and optical image fusion algorithm coupling non-local self-similarity and divergence[J]. Remote Sensing for Natural Resources, 2023, 35(1): 99-106.
[11] HE Binfang, YAO Yun, FENG Yan, LIU Huimin, DAI Juan. Sentinel-1A based flood inundation monitoring in Anhui Province during the plum rain period of 2020[J]. Remote Sensing for Natural Resources, 2023, 35(1): 140-147.
[12] DONG Jihong, MA Zhigang, LIANG Jingtao, LIU Bin, ZHAO Cong, ZENG Shuai, YAN Shengwu, MA Xiaobo. A comparative study of the identification of hidden landslide hazards based on time series InSAR techniques[J]. Remote Sensing for Natural Resources, 2022, 34(3): 73-81.
[13] LUO Xuewei, XIANG Xiqiong, LYU Yadong. PS correction of InSAR time series deformation monitoring for a certain collapse in Longli County[J]. Remote Sensing for Natural Resources, 2022, 34(3): 82-87.
[14] ZHANG Zhihua, HU Changtao, ZHANG Zhen, YANG Shuwen. PS-InSAR-based monitoring and analysis of surface subsidence in Shanghai[J]. Remote Sensing for Natural Resources, 2022, 34(3): 106-111.
[15] LI Zhu, FAN Hongdong, GAO Yantao, XU Yaozong. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field[J]. Remote Sensing for Natural Resources, 2022, 34(3): 138-145.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech