Please wait a minute...
 
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 98-104     DOI: 10.6046/zrzyyg.2020427
|
A SAR image classification method based on an improved OGMRF-RC model
LI Yuan1,2,3,4(), WU Lin1,2,3,5, QI Wenwen1,2,3, GUO Zhengwei1,2,3(), LI Ning1,2,3
1. College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2. Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China
3. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China
4. School of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu 476000, China
5. College of Environment and Planning, Henan University, Kaifeng 475004, China
Download: PDF(4970 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The classification of synthetic aperture Radar (SAR) images is one of the key technologies in the field of remote sensing applications. To address the problem that regional class labels affect the classification accuracy in the object-based Markov random field (OMRF) model, this paper proposes the concept of regional category fuzzy probability (RCFP) label field, which can effectively avoid the misclassification caused by wrong class labels by fully considering the possible categories of a single object. The RCFP of every region can be obtained using the regional edge information and posterior probability according to the features of the region and its adjacent regions. Then it is included in the calculation of feature field parameters to make the feature field parameters highly close to the real conditions of objects. The study area is located in the eastern part of Kaifeng City, Henan Province, covering an area of about 1 400 km2. Sentinel-1 SAR images were used for the classification experiment of farmlands, buildings, and water in the study area, and the performance of the improved method in this study was compared with that of the method of K-means, fuzzy C-means (FCM), MRF, and OGMRF-RC. The experimental results show that the overall accuracy (OA) and the Kappa coefficient of the proposed method are 94.16% and 0.8957 respectively, which are higher than those of other methods.
Keywords classification of synthetic aperture Radar (SAR) images      Markov random field (MRF)      feature filed      regional category fuzzy probability (RCFP)      Sentinel-1     
ZTFLH:  TP391  
Corresponding Authors: GUO Zhengwei     E-mail: liyuan_960423@vip.henu.edu.cn;gzw@henu.edu.cn
Issue Date: 23 December 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yuan LI
Lin WU
Wenwen QI
Zhengwei GUO
Ning LI
Cite this article:   
Yuan LI,Lin WU,Wenwen QI, et al. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020427     OR     https://www.gtzyyg.com/EN/Y2021/V33/I4/98
Fig.1  Result of super-pixel segmentation
Fig.2  Flow chart of method in this paper
Fig.3  Study area and sample distribution
参数 SAR图像
产品类型 SLC
成像模式 干涉宽幅
波段 C
成像日期 2020年2月27日
分辨率 4 m×20 m
图像尺寸 1 900像素×1 900像素
极化方式 VV极化
Tab.1  Parameters of Sentinel-1 Satellite SAR image used in experiment
Fig.4-1  SAR image classification results of five methods
Fig.4-2  SAR image classification results of five methods
Fig.5  SAR image classification results of selected area
Fig.6  OA and Kappa Values of five methods
地物类型 K-means FCM MRF OGMRF-RC 本文方法
农田 0.934 8 0.953 7 0.952 0 0.954 4 0.959 4
建筑 0.754 2 0.798 3 0.824 4 0.829 0 0.890 5
水 0.969 0 0.509 4 0.838 6 0.954 3 0.955 9
Tab.2  UA value of five methods
[1] 范文兵, 孙志远. 基于小波域广义高斯分布的SAR图像分割算法[J]. 计算机工程与应用, 2020,56(5):222-226.
[1] Fan W B, Sun Z Y. SAR image segmentation algorithm based on generalized gauss distribution in wavelet domain[J]. Computer Engineering and Applications, 2020,56(5):222-226.
[2] 来全, 张继权, 席宇阳, 等. 基于三聚类中心K-Means算法的SAR船只检测方法[J]. 东北师大学报(自然科学版), 2017,49(3):61-67.
[2] Lai Q, Zhang J Q, Xi Y Y, et al. A ship detection method for SAR imagery based on three-centroid K-Means clustering algorithm[J]. Journal of Northeast Normal University(Nature Science Edition), 2017,49(3):61-67.
[3] 韩子硕, 王春平. 基于改进FCM与MRF的SAR图像分割[J]. 系统工程与电子技术, 2019,41(8):1726-1734.
[3] Hang Z S, Wang C P. SAR image segmentation based on improved FCM and MRF[J]. Systems Engineering and Electronics, 2019,41(8):1726-1734.
[4] 国贤玉, 李坤, 王志勇, 等. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
doi: 10.6046/gtzyyg.2018.04.04
[4] Guo X Y, Li K, Wang Z Y, et al. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy[J]. Remote Sensing for Land and Resources, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
doi: 10.6046/gtzyyg.2018.04.04
[5] Voisin A, Krylov V A, Moser G, et al. Classification of very-high resolution SAR images of urban areas using copulas and texture in a hierarchical markov random field model[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(1):96-100.
doi: 10.1109/LGRS.2012.2193869 url: http://ieeexplore.ieee.org/document/6203366/
[6] Delia C, Ruscino S, Abbate M, et al. SAR image classification through information-theoretic textural features,MRF segmentation,and object-oriented learning vector quantization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(4):1116-1126.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[7] Shen Y, Chen J Y, Xiao L, et al. A bilevel contextual MRF model for supervised classification of high spatial resolution remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019,12(12):360-5372.
[8] Bi H X, Xu L, Cao X, et al. Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field[J]. IEEE Transactions on Image Processing, 2020,29:6601-6614.
doi: 10.1109/TIP.2020.2992177 url: https://ieeexplore.ieee.org/document/9106810/
[9] Jiang X F, Zhang Y, Li Y, et al. Hyperspectral image classification with transfer learning and Markov random fields[J]. IEEE Geoscience and Remote Sensing Letters, 2020,17(3):544-548.
doi: 10.1109/LGRS.8859 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859
[10] Lee S H. Hierarchical classification of land-cover types using RAG-based merging[C]//2007 IEEE International Geoscience and Remote Sensing Symposium, 2007:2060-2062.
[11] Chen X H, Zheng C, Yao H T, et al. Image segmentation using a unified Markov random field model[J]. The Institution of Engineering and Technology, 2017,11(10):860-869.
[12] Zheng C, Yao H T. Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients[J]. International Journal of Remote Sensing, 2019,40(11):4441-4472.
doi: 10.1080/01431161.2018.1563841 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1563841
[13] Xu X F, Zou B, Zhang L M. PolSAR image classification based on object-based Markov random field with polarimetric auxiliary label field[J]. IEEE Geoscience and Remote Sensing Letters, 2020,17(9):1558-1562.
doi: 10.1109/LGRS.8859 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859
[14] 朱阿兴, 闾国年, 周成虎, 等. 地理相似性:地理学的第三定律?[J]. 地球信息科学学报, 2020,22(4):673-679
doi: 10.12082/dqxxkx.2020.200069
[14] Zhu A X, Lv G N, Zhou C H, et al. Geographic similarity:Third law of geography?[J]. Journal of Geo-information Science, 2020,22(4):673-679.
[15] 李智, 曲长文, 周强, 等. 基于SLIC超像素分割的SAR图像海陆分割算法[J]. 雷达科学与技术, 2017,15(4):354-358
[15] Li Z, Qu C W, Zhou Q, et al. A sea-land segmentation algorithm of SAR image based on the SLIC superpixel division[J]. Radar Science and Technology, 2017,15(4):354-358.
[1] 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.
[2] ZHANG Hao, GAO Xiaohong, SHI Feifei, LI Runxiang. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J]. Remote Sensing for Natural Resources, 2022, 34(4): 144-154.
[3] WANG Yu, ZHOU Zhongfa, WANG Lingyu, LUO Jiancheng, HUANG Denghong, ZHANG Wenhui. Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas[J]. Remote Sensing for Natural Resources, 2022, 34(4): 155-165.
[4] ZHANG Shu, ZHOU Zhongfa, WANG Lingyu, CHEN Quan, LUO Jiancheng, ZHAO Xin. Inversion of moisture in surface soil of farmland in karst mountainous areas using multi-temporal SAR images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 154-163.
[5] ZHA Dongping, CAI Haisheng, ZHANG Xueling, HE Qinggang. Extraction of paddy fields using multi-temporal Sentinel-1 images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 184-195.
[6] WEI Chang, FU Bolin, QIN Jiaoling, WANG Yanan, CHEN Zhihan, LIU Bing. Monitoring of spatial-temporal dynamic changes in water surface in marshes based on multi-temporal Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2022, 34(2): 251-260.
[7] YANG Xianhua, WEI Peng, LYU Jun, HAN Lei, SHI Haolin, LIU Zhi. Application of mining collapse recognition technology based on multi-source remote sensing[J]. Remote Sensing for Natural Resources, 2022, 34(2): 162-167.
[8] SHI Min, GONG Huili, CHEN Beibei, GAO Mingliang, ZHANG Shunkang. Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 55-63.
[9] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[10] ZHANG Teng, XIE Shuai, HUANG Bo, FAN Jinghui, CHEN Jianping, TONG Liqiang. Detection of active landslides in central Maoxian County using Sentinel-1 and ALOS-2 data[J]. Remote Sensing for Land & Resources, 2021, 33(2): 213-219.
[11] SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
[12] Jingjian LIU, Hongzhong LI, Cui HUA, Yuman SUN, Jinsong CHEN, Yu HAN. Extraction of early paddy rice area in Lingao County based on Sentinel-1A data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 191-199.
[13] Zhenlin WANG, Mingsheng LIAO, Lu ZHANG, Heng LUO, Jie DONG. Detecting and characterizing deformations of the left bank slope near the Jinping hydropower station with time series Sentinel-1 data[J]. Remote Sensing for Land & Resources, 2019, 31(2): 204-209.
[14] Zechao BAI, Baocun WANG, Guowang JIN, Qing XU, Hongmin ZHANG, Hui LIU. Applicability analysis of ground deformation monitoring in mining area by Sentinel - 1A data[J]. Remote Sensing for Land & Resources, 2019, 31(2): 210-217.
[15] Xiaobo ZHANG, Xuesheng ZHAO, Daqing GE, Bin LIU, Ling ZHANG, Man LI, Yan WANG. Subsidence monitoring of Huainan coal mine from Sentinel TOPS images based on Stacking technique[J]. Remote Sensing for Land & Resources, 2018, 30(4): 200-205.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech