基于Sentinel-2卫星影像的面向对象城市水体提取
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摘要
以新发射的哨兵2号多光谱卫星影像为基础数据源,对城市水体信息进行了面向对象的提取研究。融合大气校正后的哨兵2号卫星10 m分辨率的可见近红外影像和20 m分辨率的短波影像,在多尺度分割的基础上,分析了影像对象的光谱和空间信息,建立了知识规则集;手动调节阈值,实现了水体信息的半自动提取。选取了城区和郊区两个研究区进行实验,总体精度均在95%以上,Kappa系数在0.75以上。结果表明,本文的水体提取方法取得了较好的效果,且城市主城区的水体提取精度高于郊区。
In this paper, based on the newly launched Sentinel-2 multi-spectral instrument(MSI) imagery, we put forward a object-oriented urban water body extraction method. Firstly, we utilized four Near-Infra-Red(NIR) bands in 10 m resolution as a high-resolution image to sharpen the 20 m Short-Wavelength Infrared(SWIR) band after atmospheric correction. Then, we used the multiresolution segmentation algorithm to segment the combined images, and established the knowledge rule sets through the analysis of spectral and spatial information of image objects. Finally, we realized the semi-automatic extraction of water body by adjusting the threshold manually. We chose two study areas with different characteristics, and the total accuracies were above 95% and Kappa values were more than 0.75. The experiment results indicate that the proposed method has a better performance and higher accuracy in urban center region than suburbs.
In this paper, based on the newly launched Sentinel-2 multi-spectral instrument(MSI) imagery, we put forward a object-oriented urban water body extraction method. Firstly, we utilized four Near-Infra-Red(NIR) bands in 10 m resolution as a high-resolution image to sharpen the 20 m Short-Wavelength Infrared(SWIR) band after atmospheric correction. Then, we used the multiresolution segmentation algorithm to segment the combined images, and established the knowledge rule sets through the analysis of spectral and spatial information of image objects. Finally, we realized the semi-automatic extraction of water body by adjusting the threshold manually. We chose two study areas with different characteristics, and the total accuracies were above 95% and Kappa values were more than 0.75. The experiment results indicate that the proposed method has a better performance and higher accuracy in urban center region than suburbs.
引文
[1]DU Y,ZHANG Y,LING F,et al.Water Bodies’Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10 m Spatial Resolution Produced by Sharpening the SWIR Band[J].Remote Sensing,2016,8(4):354
[2]Mc Feeters 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):1 425-1 432
[3]XU H Q.Modification of Normalised Difference Water Index(NDWI)to Enhance Open Water Features in Remotely Sensed Imagery[J].International Journal of Remote Sensing,2006,27(14):3 025-3 033
[4]丁凤.基于新型水体指数(NWI)进行水体信息提取的实验研究[J].测绘科学,2009,34(4):155-157
[5]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):23-35
[6]LI B,ZHANG H,XU F.Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features[C]//IOP Conference Series:Earth and Environmental Science,IOP Publishing,2014
[7]Blaschke T.Object Based Image Analysis for Remote Sensing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16
[8]杜斌.基于面向对象的高分辨率遥感影像水体信息提取优势研究[D].昆明:云南师范大学,2014
[9]HE Y R,ZHANG X X,HUA L Z.Object-based Distinction Between Building Shadow and Water in High-resolution Imagery Using Fuzzy-rule Classification and Artificial Bee Colony Optimization[J].Journal of Sensors,2016(7):36-41
[10]Martimor P,Arino O,Berger M,et al.Sentinel-2:ESA's Optical High-resolution Mission for GMES Operational Services[J].Remote Sensing of Environment,2012(120):25-36
[11]YANG X C,ZHAO S S,QIN X B,et al.Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution Via NDWI-based Image Sharpening[J].Remote Sensing,2017,9(6):596
[12]Kaplan G,Avdan U.Object-based Water Body Extraction Model Using Sentinel-2 Satellite Imagery[J].European Journal of Remote Sensing,2017,50(1):137-143
[13]ZHOU Y,DONG J W,XIAO X M,et al.Open Surface Water Mapping Algorithms:a Comparison of Water-related Spectral Indices and Sensors[J].Water,2017,9(4):256
[14]苏伟,张明政,蒋坤萍,等.Sentinel-2卫星影像的大气校正方法研究[J].光学学报,2018,38(1):322-331
[15]ZHANG T X,SU J Y,LIU C J,et al.Band Selection in Sentinel-2 Satellite for Agriculture Applications[J].International Conference on Automation and Computing,2017(8):356-368
[16]蒋丹丹,卢刚,陈成.基于单极化TerraSAR-X影像提取建筑区研究[J].地理与地理信息科学,2016,32(1):60-65
[2]Mc Feeters 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):1 425-1 432
[3]XU H Q.Modification of Normalised Difference Water Index(NDWI)to Enhance Open Water Features in Remotely Sensed Imagery[J].International Journal of Remote Sensing,2006,27(14):3 025-3 033
[4]丁凤.基于新型水体指数(NWI)进行水体信息提取的实验研究[J].测绘科学,2009,34(4):155-157
[5]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):23-35
[6]LI B,ZHANG H,XU F.Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features[C]//IOP Conference Series:Earth and Environmental Science,IOP Publishing,2014
[7]Blaschke T.Object Based Image Analysis for Remote Sensing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16
[8]杜斌.基于面向对象的高分辨率遥感影像水体信息提取优势研究[D].昆明:云南师范大学,2014
[9]HE Y R,ZHANG X X,HUA L Z.Object-based Distinction Between Building Shadow and Water in High-resolution Imagery Using Fuzzy-rule Classification and Artificial Bee Colony Optimization[J].Journal of Sensors,2016(7):36-41
[10]Martimor P,Arino O,Berger M,et al.Sentinel-2:ESA's Optical High-resolution Mission for GMES Operational Services[J].Remote Sensing of Environment,2012(120):25-36
[11]YANG X C,ZHAO S S,QIN X B,et al.Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution Via NDWI-based Image Sharpening[J].Remote Sensing,2017,9(6):596
[12]Kaplan G,Avdan U.Object-based Water Body Extraction Model Using Sentinel-2 Satellite Imagery[J].European Journal of Remote Sensing,2017,50(1):137-143
[13]ZHOU Y,DONG J W,XIAO X M,et al.Open Surface Water Mapping Algorithms:a Comparison of Water-related Spectral Indices and Sensors[J].Water,2017,9(4):256
[14]苏伟,张明政,蒋坤萍,等.Sentinel-2卫星影像的大气校正方法研究[J].光学学报,2018,38(1):322-331
[15]ZHANG T X,SU J Y,LIU C J,et al.Band Selection in Sentinel-2 Satellite for Agriculture Applications[J].International Conference on Automation and Computing,2017(8):356-368
[16]蒋丹丹,卢刚,陈成.基于单极化TerraSAR-X影像提取建筑区研究[J].地理与地理信息科学,2016,32(1):60-65
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