The construction and verification of a water index in the complex environment based on GF-2 images
-
摘要: 高分二号(GF-2)影像的高空间分辨率有助于获得更为准确的水体分布信息。针对现有水体指数难以应对复杂环境、高空间分辨率的遥感影像水体提取时易出现“椒盐”现象的问题,基于GF-2影像进行了水体指数的构建及验证。首先,通过分析各地表覆盖物的波谱信息,构建了一种综合水体指数(comprehensive water index,CWI),并进行精度验证; 其次,通过图像分割结合水体指数进行水体提取并进行精度验证; 然后,为了充分利用光谱信息和发挥分类器的优势,将分割后同质对象的光谱信息与水体指数组合作为分类器的输入数据,进行水体提取并进行精度验证; 最后,验证综合水体指数在WorldView-2影像和GF-1影像的适用性。经过研究可知: ①新构建的综合水体指数在进行水体提取时,能够有效抑制阴影、建筑物、道路、植被、裸土等地表覆盖的影响,精度明显提高; ②通过图像分割结合水体指数提取水体信息能有效抑制“椒盐”现象的产生; ③分类器结合水体指数能有效提高水体提取精度; ④综合水体指数同样适用WorldView-2影像和GF-1影像。综上分析,综合水体指数能够有效地提取水体信息,可用于河流、湖泊的提取和更新,池塘养殖面积的调查等,是一种高精度的水体提取方法。Abstract: The high spatial resolution of GF-2 images helps to obtain more accurate water distribution information. This study constructed a water index based on GF-2 images and verified it, aiming to solve the problem that the salt and pepper noise is prone to occur when the existing water indices are used to extract information on water bodies in complex environments from high-resolution remote sensing images. Firstly, this study established a comprehensive water index (CWI) by analyzing the spectral information of surface coverings and verified its precision. Secondly, information on water bodies was extracted through image segmentation combined with the CWI, and the extraction precision was verified. Then, to fully utilize the spectral information and the advantages of a classifier, the spectral information on the segmented homogeneous objects and the CWI were combined as the input data of the classifier to extract information on water bodies and verify the extraction precision. Finally, this study verified the applicability of the CWI in both WorldView-2 and GF-1 images. The results are as follows. ① The newly constructed CWI can effectively suppress the impacts of surface coverings, such as shadow, buildings, roads, vegetation, and bare soil, thus significantly improving the extraction precision. ② Extracting information on water bodies through image segmentation combined with the CWI can effectively inhibit the occurrence of the pepper and salt noise. ③ A classifier combined with a water index can effectively improve the information extraction precision of water bodies. ④ The CWI is applicable to both WorldView-2 and GF-1 images. In sum, the CWI can be used to effectively extract information on water bodies and applies to the information extraction and renewal of rivers and lakes and the surveys of the cultivation area of pounds and thereby is a high-precision method for extraction information of water bodies.
-
-
[1] Yang C J, Wei Y M, Wang S Y, et al. Extracting the flood extent from satellite SAR image with the support of topographic data[C]// International Conferences on Info-Tech and Info-Net.IEEE Computer Society, 1998.
[2] 何红术, 黄晓霞, 李红旮, 等. 基于改进U-Net网络的高分遥感影像水体提取[J]. 地球信息科学学报, 2020, 22(10):2010-2022.
[3] He H S, Huang X X, Li H G, et al. Water body extraction of high resolution remote sensing image basedon improved U-Net network[J]. Journal of Geo-Information Science, 2020, 22(10):2010-2022.
[4] 梁泽毓, 吴艳兰, 杨辉, 等. 基于密集连接全卷积神经网络的遥感影像水体全自动提取方法[J]. 遥感信息, 2020, 35(4):68-77.
[5] Liang Z Y, Wu Y L, Yang H, et al. Full-automatic water extraction method for remote sensing image based on densely connected full convolutional neural network[J]. Remote Sensing Information, 2020, 35(4):68-77
[6] 陈前, 郑利娟, 李小娟, 等. 基于深度学习的高分遥感影像水体提取模型研究[J]. 地理与地理信息科学, 2019, 35(4):43-49.
[7] Chen Q, Zheng L J, Li X J, et al. Water boby extraction form high-resolution satellite remote sensing images based on deep learning[J]. Geography and Geo-Information Science, 2019, 35(4):43-49.
[8] Gulcan S, Mehmet O. Water body extraction and change detection using time series: A case study of Lake Burdur,Turkey[J]. Journal of Taibah University for Science, 2017, 11(3):381-391.
[9] 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.
[10] Wang J J, Ding J L, Zhang C, et al. Method of water information extraction by improved 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.
[11] 毛莹莹, 陈友飞. 基于面向对象法的Landsat8影像山区细小水体提取方法[J]. 亚热带资源与环境学报, 2015, 10(4):86-92.
[12] Mao Y Y, Chen Y F. Water body extraction method based on object-oriented in mountainous area with Landsat8 image[J]. Journal of Subtropical Resources and Environment, 2015, 10(4):86-92.
[13] 胡卫国, 孟令奎, 张东映, 等. 资源一号02C星图像水体信息提取方法[J]. 国土资源遥感, 2014, 26(2):43-47.doi: 10.6046/gtzyyg.2014.02.08.
[14] Hu W G, Meng L K, Zhang D Y, et al. Method of water extraction from ZY-1 02C satellite image[J]. Remote Sensing for Land and Resources, 2014, 26(2):43-47.doi: 10.6046/gtzyyg.2014.02.08.
[15] 王小标, 谢顺平, 都金康. 水体指数构建及其在复杂环境下有效性研究[J]. 遥感学报, 2018, 22(2):360-372.
[16] Wang X B, Xie S P, Du J K. Water index formulation and its effectiveness research on the complicated surface water surroundings[J]. Journal of Remote Sensing, 2018, 22(2): 360-372.
[17] 杨树文, 李轶鲲, 刘涛, 等. 基于SPOT5影像自动提取水体的新方法[J]. 武汉大学学报(信息科学版), 2015, 40(3):308-314.
[18] Yang S W, Li Y K, Liu T, et al. A new automatic water boby feature extraction method based on SPOT5 images[J]. Geomatics and Information Science of Wuhan University, 2015, 40(3):308-314.
[19] Gudina L F, Henrik M, Rasmus F, 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.
[20] 沈占锋, 夏列钢, 李均力, 等. 采用高斯归一化水体指数实现遥感影像河流的精确提取[J]. 中国图象图形学报, 2013, 18(4):421-428.
[21] Shen Z F, Xia L G, Li J L, et al. Automatic and high-precision extraction of rivers from remotely sensed images with Gaussian normalized water index[J]. Journal of Image and Graphics, 2013, 18(4):421-428.
[22] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5):589-595.
[23] Xu H Q. A study on information extraction of water body with the modified normalized difference water index (MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5):589-595.
[24] Rogers A S, Kearney M S. Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices[J]. International Journal of Remote Sensing, 2004, 25(12):2317-2335.
[25] 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.
[26] 段纪维, 钟九生, 江丽, 等. 基于GF-2影像的雨后洪涝区超绿水体指数提取方法[J]. 地理与地理信息科学, 2021, 37(3):35-41.
[27] Duan J W, Zhong J S, Jiang L, et al. Extraction method of ultra-green water index for flood area after rain based on GF-2 image[J]. Geography and Geo-Information Science, 2021, 37(3):35-41.
[28] 邹橙, 杨学志, 董张玉, 等. 基于GF-2遥感影像的一种快速水体信息提取方法[J]. 图学学报, 2019, 40(1):99-104.
[29] Zhou C, Yang X Z, Dong Z Y, et al. A fast water information extraction method based on GF-2 remote sensing image[J]. Journal of Graphics, 2019, 40(1):99-104.
[30] 谷金英, 晏明, 张晓娇, 等. 利用高分一号影像提取水体信息的方法研究[J]. 农业与技术, 2018, 38(11):24-26,109.
[31] Gu J Y, Yan M, Zhang X J, et al. Research on the method of extracting water information from gaofen-1 image[J]. Agriculture and Technology, 2018, 38(11):24-26,109.
[32] 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.
[33] 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.
[34] 王冬梅, 陈琳, 冯峰. 面向对象的GF-2影像水体信息提取研究[J]. 人民黄河, 2021, 43(5):80-83,90.
[35] Wand D M, Chen L, Feng F. Study on water information extraction from GF-2 image based on object-oriented method[J]. Yellow River, 2021, 43(5):80-83,90.
[36] 黄帅, 丁建丽, 李艳华. 面向对象的国产GF-1遥感影像水体信息提取研究[J]. 人民长江, 2016, 47(5):23-28.
[37] Huang S, Ding J L, Li Y H. Study of water information extraction based on domestic GF-1 remote sensing image by using object-oriented method[J]. Yangtze River, 2016, 47(5):23-28.
[38] 洪亮, 黄雅君, 杨昆, 等. 复杂环境下高分二号遥感影像的城市地表水体提取[J]. 遥感学报, 2019, 23(5):871-882.
[39] Hong L, Huang Y J, Yang K, et al. Study on urban surface water extraction from heterogeneous environments using GF-2 remotely sensed images[J]. Journal of Remote Sensing, 2019, 23(5): 871-882.
[40] 王胜蕾. 基于水色指数的大范围长时序湖库水质遥感监测研究[D]. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2018.
[41] Wang S L. Large-scale and long-time water quality remote sensing monitoring over lakes based on water color index[D]. Beijing: University of Chinese Academy of Sciences, 2018.
[42] 段秋亚, 孟令奎, 樊志伟, 等. GF-1卫星影像水体信息提取方法的适用性研究[J]. 国土资源遥感, 2015, 27(4):79-84.doi: 10.6046/gtzyyg.2015.04.13.
[43] Duan Q Y, Meng L K, Fan Z W, et al. Applicability of the water information extraction method based on GF-1 image[J]. Remote Sensing for Land and Resources, 2015, 27(4):79-84.doi: 10.6046/gtzyyg.2015.04.13.
[44] Jain S K, Singh R D, Jain M K, et al. Delineation of flood-prone areas using remote sensing techniques[J]. Water Resources Management, 2005, 19(4):333-347.
[45] Zhang F, Li J, Zhang B, et al. A simple automated dynamic thresho-ld extraction method for the classification of large water bodies from Landsat8 OLI water index images[J]. International Journal of Remote Sensing, 2018, 39(11):3429-3451.
[46] Fieuzal R, Sicre C M, Baup F. Estimation of corn yield using multi-temporal optical and Radar satellitedata and artificial neural networks[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 57(1):14-23.
[47] 刘双童, 王明孝, 杨树文, 等. 基于GF-2高分辨率遥感影像的水体提取方法研究[J]. 全球定位系统, 2018, 43(6):37-43.
[48] Liu S T, Wang M X, Yang S W, et al. Research on water body extraction method based on GF-2 high resolution remote sensing image[J]. GNSS World of China, 2018, 43(6):37-43.
-
计量
- 文章访问数: 594
- PDF下载数: 20
- 施引文献: 0
下载: