Application of U-net model to water extraction with high resolution remote sensing data

doi:10.6046/gtzyyg.2020.01.06

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(3137 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In this paper, the authors used a U-net model to conduct water extraction, and the result was compared with that of the random forest model. The accuracy of the U-net model was validated by using GF-1 images in Chaohu Lake Basin. The results show that both models are of high accuracy for large area of water body, but random forest model has more spots for small area of water body, and the result of U-net model is more consistent with the manual visual interpretation result. Moreover, the U-net model can effectively remove the shadows of mountains and buildings. The result indicates that U-net model performs better than random forest model with the overall accuracy of 98.69%, Kappa coefficient of 0.95, omission error of 1.90% and commission error of 1.18%. In contrast, the overall accuracy, Kappa coefficient, omission error and commission error of random forest model are about 98.05%, 0.92, 1.61% and 2.99%, respectively. In addition, the classification features for traditional machine learning model are always calculated by manual extraction. However, the inputs for U-net model are the 4 band spectrum data of GF-1 images. These data suggest that the U-net model avoids the process of manually extracting classification features and has a higher degree of automation. It should be noted that the U-net model uses more train samples with less time-consuming. It is believed that this model can significantly improve the surface water detection accuracy and can be used for the automatic renewal of a larger range of water bodies.

Keywords GF-1 U-net random forest water extraction

TP79

Corresponding Authors: Jiahua CHENG E-mail: dhsziyuan@163.com

Issue Date: 14 March 2020

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Ning WANG
	Jiahua CHENG
	Hanye ZHANG
	Hongjie CAO
	Jun LIU

Cite this article:

Ning WANG,Jiahua CHENG,Hanye ZHANG, et al. Application of U-net model to water extraction with high resolution remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 35-42.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.06 OR https://www.gtzyyg.com/EN/Y2020/V32/I1/35

Fig.1 Location of the study area

Fig.2 Structure of the U-net model

评价指标	计算方法
总体精度	正确分类像元数占总像元数的比例
Kappa系数	Kappa= $N ∑ x ii - ∑ (x i · x · i) N 2 - ∑ (x i · x · i) ①$
漏分误差	水体错分为非水体的像元数占真实水体总像元数的比例
错分误差	非水体错分为水体的像元数占分类得到水体总像元数的比例

Tab.1 Evaluation indexes on remote sensing image classification results

Fig.3 Comparison of the water extraction results between U-net and random forest

Fig.4 Comparison of the extraction results for small water bodies

Fig.5 Comparison of shadow removal results by U-net and random forest

Tab.2 Comparison of water extraction accuracy

Tab.3 Efficiency of different models for water extraction

[1]	Pekel J, Cottam A, Gorelick N , et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016,540(7633):418-422.
[2]	Khandelwal A, Karpatne A, Marlier M E , et al. An approach for global monitoring of surface water extent variations in reservoirs using MODIS data[J]. Remote Sensing of Environment, 2017,202:113-128.
[3]	Rao P Z, Jiang W G, Hou Y K , et al. Dynamic change analysis of surface water in the Yangtze River Basin based on MODIS products[J]. Remote Sensing, 2018,10(7):1025.
[4]	Wang Z F, Liu J G, Li J B , et al. Multi-spectral water index (MuWI):A native 10-m multi-spectral water index for accurate water mapping on Sentinel-2[J]. Remote Sensing, 2018,10(10):1643.
[5]	Yao F F, Wang C, Dong D , et al. High-resolution mapping of urban surface water using ZY-3 multi-spectral imagery[J]. Remote Sensing, 2015,7(9):12336-12355.
[6]	Yang F, Guo J, Tan H , et al. Automated extraction of urban water bodies from ZY-3 multi-spectral imagery[J]. Water, 2017,9(2):144.
[7]	陈文倩, 丁建丽, 李艳华 , 等. 基于国产GF-1遥感影像的水体提取方法[J]. 资源科学, 2015,37(6):1166-1172.
[7]	Chen W Q, Ding J L, Li Y H , et al. Extraction of water information based on China-made GF-1 remote sense image[J]. Resources Science, 2015,37(6):1166-1172.
[8]	王瑾杰, 丁建丽, 张成 , 等. 基于GF-1卫星影像的改进SWI水体提取方法[J]. 国土资源遥感, 2017,29(1):29-35.doi: 10.6046/gtzyyg.2017.01.05.
[8]	Wang J J, Ding J L, Zhang C , et al. Method of water information extraction by improve SWI based on GF-1 satellite image[J]. Remote Sensing for Land and Resources, 2017,29(1):29-35.doi: 10.6046/gtzyyg.2017.01.05.
[9]	毕海云, 王思远, 曾江源 , 等. 基于TM影像的几种常用水体提取方法的比较和分析[J]. 遥感信息, 2012,27(5):77-82
[9]	Bi H Y, Wang S Y, Zeng J Y , et al. Comparison and analysis of several common water extraction methods based on TM image[J]. Remote Sensing Information, 2012,27(5):77-82
[10]	Mcfeeters S K . The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996,17(7):1425-1432.
[11]	Xu H Q . Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006,27(14):3025-3033.
[12]	Ogilvie A, Belaud G, Massuel S , et al. Surface water monitoring in small water bodies:Potential and limits of multi-sensor Landsat time series[J]. Hydrology and Earth System Sciences, 2018,22(8):1-35.
[13]	Fisher A, Danaher T . A water index for SPOT5 HRG satellite imagery,New South Wales,Australia,determined by linear discriminant analysis[J]. Remote Sensing, 2013,5(11):5907-5925.
[14]	Feyisa G L, Meilby H, Fensholt R , et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014,140(1):23-35.
[15]	Wang S D, Baig M H A, Zhang L F , et al. A simple enhanced water index (EWI) for percent surface water estimation using Landsat data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(1):90-97.
[16]	Guo Q D, Pu R L, Li J L , et al. A weighted normalized difference water index for water extraction using Landsat imagery[J]. International Journal of Remote Sensing, 2017,38(19):5430-5445.
[17]	刁淑娟, 刘春玲, 张涛 , 等. 基于SVM的湖泊咸度等级遥感信息提取方法——以内蒙古巴丹吉林沙漠为例[J]. 国土资源遥感, 2016,28(4):114-118.doi: 10.6046/gtzyyg.2016.04.18.
[17]	Diao S J, Liu C L, Zhang T , et al. Extraction of remote sensing information for lake salinity level based on SVM:A case from Badain Jaran desert in Inner Mongolia[J]. Remote Sensing for Land and Resources, 2016,28(4):114-118.doi: 10.6046/gtzyyg.2016.04.18.
[18]	Feng Q L, Liu J T, Gong J H . Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier:A case of Yuyao,China[J]. Water, 2015,7:1437-1455.
[19]	Badrinarayanan V, Kendall A, Cipolla R . SegNet:A deep convolutional encoder-decoder architecture for image segmentation[EB/OL]. (2015-11-02). https://arxiv.org/abs/1511.00561. url: https://arxiv.org/abs/1511.00561
[20]	Noh H, Hong S, Han B . Learning deconvolution network for semantic segmentation[C]// IEEE International Conference on Computer Vision.Santiago:IEEE, 2015: 1520-1528.
[21]	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, 2016,40(4):834-848.
[22]	Ronneberger O, Fischer P, Brox T . U-Net:Convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer, 2015: 234-241.
[23]	伍广明, 陈奇, Shibasaki R , 等. 基于U型卷积神经网络的航空影像建筑物检测[J]. 测绘学报, 2018,47(6):864-872.
[23]	Wu G M, Chen Q, Shibasaki R , et al. High precision building detection from aerial imagery using a U-Net like convolutional architecture[J]. Acta Geodaetica et Cartographica Sinica, 2018,47(6):864-872.
[24]	苏健民, 杨岚心, 景维鹏 . 基于U-Net的高分辨率遥感图像语义分割方法[J]. 计算机工程与应用, 2019,55(7):207-213.
[24]	Su J M, Yang L X, Jing W P . A U-Net based semantic segmentation method for high resolution remote sensing image[J]. Computer Engineering and Applications, 2019,55(7):207-213.
[25]	Breiman L . Random forests[J]. Machine Learning, 2001,45(1):5-32.
[26]	陈辰, 周拥军, 李元祥 , 等. 基于U-net分割和HEIV模型的遥感图像配准方法[EB/OL]. (2018-11-30). https://kns.cnki.net/KCMS/detail/31.1289.tp.20181129.1323.005.html. url: https://kns.cnki.net/KCMS/detail/31.1289.tp.20181129.1323.005.html
[26]	Chen C, Zhou Y J, Li Y X , et al. Remote sensing image registration based on U-net segmentation and the HEIV model[EB/OL]. (2018-11-30). https://kns.cnki.net/KCMS/detail/31.1289.tp.20181129.1323.005.html. url: https://kns.cnki.net/KCMS/detail/31.1289.tp.20181129.1323.005.html
[27]	李炳亚, 潘剑君, 夏超 , 等. 基于空间位置关系的山地湖泊水体提取方法研究[J]. 遥感技术与应用, 2016,31(5):983-993.
[27]	Li B Y, Pan J J, Xia C , et al. Study on the extraction method of lake water body in mountainous based on spatial position relation[J]. Remote Sensing Technology and Application, 2016,31(5):983-993.

[1]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[2]	LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[3]	GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[4]	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.
[5]	WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[6]	WU Yu, ZHANG Jun, LI Yixu, HUANG Kangyu. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
[7]	WU Qian, JIANG Qigang, SHI Pengfei, ZHANG Lili. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.
[8]	SU Longfei, LI Zhengxuan, GAO Fei, YU Min. A review of remote sensing image water extraction[J]. Remote Sensing for Land & Resources, 2021, 33(1): 9-11.
[9]	XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[10]	WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[11]	YANG Lijuan. Estimating PM_2.5 concentrations in eastern coastal area of China using a two-stage random forest model[J]. Remote Sensing for Land & Resources, 2020, 32(4): 137-144.
[12]	LI Guoqing, HUANG Jinghua, LIU Guan, LI Jie, ZHAI Bochao, DU Sheng. A study of the landscape fragmentations of land cover structure based on Landsat8 remote sensing image: A case study of Mata watershed in Yan’an, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 121-128.
[13]	LI Xusheng, LIU Yufeng, CHEN Donghua, LIU Saisai, LI Hu. Cloud detection based on support vector machine with image features for GF-1 data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 55-62.
[14]	Yizhe WANG, Guo LIU, Li GUO, Shihu ZHAO, Xueli ZHANG. Research on ortho-rectification and true color synthesis technique of GF-1 WFV data in China-Pakistan Economic Corridor[J]. Remote Sensing for Land & Resources, 2020, 32(2): 213-218.
[15]	Yuting YANG, Hailan CHEN, Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.

Viewed

Full text

Abstract

Cited

Shared

Discussed