基于WorldView-02高分影像的BP和RBF神经网络遥感水深反演

郑贵洲; 乐校冬; 王红平; 花卫华

doi:10.3799/dqkx.2017.552

Volume 42 Issue 12

Dec. 2017

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2017 > 42(12): 2345-2353

Zheng Guizhou, Le Xiaodong, Wang Hongping, Hua Weihua, 2017. Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network. Earth Science, 42(12): 2345-2353. doi: 10.3799/dqkx.2017.552

Citation:

Zheng Guizhou, Le Xiaodong, Wang Hongping, Hua Weihua, 2017. Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network. Earth Science, 42(12): 2345-2353. doi: 10.3799/dqkx.2017.552

Citation:

PDF( 2297 KB)

Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network

doi: 10.3799/dqkx.2017.552

Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Received Date: 2017-01-24
Publish Date: 2017-12-15

Abstract

Abstract

The inversion of water depth from remote sensing imagery is an important technology of depth measurement. In this paper, on the basis of radiometric calibration and atmospheric correction, BP(back propagation)and RBF(radial basis function) neural networks were built to retrieve water depth from WorldView-02 high-resolution satellite imagery in Mischief reef. Band 1 to band 8 of satellite imagery were used as the input data of the neural networks. Then, they were converted from input layer to hidden layer and from the hidden layer to output layer with tansig, logsig, Gaussian and purelin functions. Finally, the accuracy of the two models was evaluated by R² (coefficient of determination), MAE(mean absolute error), RMSE(root mean square error) and the regression analysis between retrieved water depth and ground measured water depth. The results show that RBF neural network has simpler model structure, and lower requirement of samples. Besides, its retrieval accuracy reaches 0.995. Therefore, RBF neural network is more suitable for the inversion of water depth.
- remote sensing,
- WorldView-02,
- water depth inversion,
- BP neural network,
- RBF neural network

FullText(HTML)

References(34)

References

Ceyhun, Ö., Yalcin, A., 2010.Remote Sensing of Water Depths in Shallow Waters via Artificial Neural Networks.Estuarine Coastal and Shelf Science, 89(1):89-96. doi: 10.1016/j.ecss.2010.05.015

Anctil, F., Coulibaly, P., 2004.Wavelet Analysis of the Interannual Variability in Southern Québec Streamflow.Journal of Climate, 17(1):163-173.doi:10.1175/1520-0442(2004)017<0163:WAOTIV>2.0.CO;2

Dang, F.X., Ding, Q., 2003.The Use of Multi-Band Satellite Data to Study Shallow Water Depth Inversion.Marine Science Bulletin, 22(3):55-60(in Chinese).

Deng, Z.D., Ye, X., Guan, H.J., et al., 2013.Remote Sensing of Water Depth Based on RBF Neural Network.Journal of PLA University of Science and Technology (Natural Science Edition), 14(1):101-106(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFJL201301020.htm

Feng, H.J., Zhou, A.G., Yu, J.J., et al., 2016.A Comparative Study on Plum-Rain Triggered Landslide Susceptibility Assessment Models in West Zhejiang Province.Earth Science, 41(3):403-415(in Chinese with English abstract). doi: 10.1186/s40677-017-0078-9

Gao, G.D., Zhang, W.X, Mu, G.Y., 2011.A Comparative Study on RBF Network and BP Network in the Model of Salinity.Marine Science Bulletin, 30(1):12-15(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-HUTB201101003.htm

Gao, W., Liu, X.G., Peng, P., Chen, Q.H., 2010.An Improved Method of High-Resolution Remote Sense Image Segmentation.Earth Science, 35(3):421-425 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201003020.htm

Han, X.Q., Su, Y., Li, J., et al., 2012.Atmospheric Correction and Verification of the SPOT Remote Sensing Image in Coastal Zones.Geographical Research, 31(11):2007-2016(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLYJ201211010.htm

Huang, W.Q., Wu, D., Yang, Y., et al., 2003.Multi-Spectral Remote Sensing Water Depth Retrieval Technique in Shallow Sea.Ocean Technology, 32(2):43-46(in Chinese with English abstract).

Liu, M.T., Zhang, C.C., Tian, Z.Z., et al., 2016.RBF Neural Networks Based-Study on Data Fusion for Measurement of Sediment Concentration of Yellow River.Water Resources and Hydropower Engineering, 46(1):126-130(in Chinese with English abstract).

López-Serrano, P.M., Corral-Rivas, J.J., et al., 2016.Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data.Remote Sensing, 8(5):369.doi: 10.3390/rs8050369

Lyzenga, D.R., 1978.Passive Remote Sensing Techniques for Mapping Water Depth and Botton Features.Applied Optics, 17(3):379-383.doi: 10.1364/AO.17.000379

Ma, H.Z., Liu, S.M., 2016.The Potential Evaluation of Multisource Remote Sensing Data for Extracting Soil Moisture Based on the Method of BP Neural Network.Canadian Journal of Remote Sensing, 42(2):117-124. doi: 10.1080/07038992.2016.1160773

Poupardin, A., Idier, D., De Michele, M., et al., 2015.Water Depth Inversion from a Single SPOT-5 Dataset.IEEE Transactions on Geoscience and Remote Sensing, 54(4):2329-2342.doi: 10.1109/TGRS.2015.2499379

Su, H.B., Liu, H.X., Wu, Q.S., 2015.Prediction of Water Depth From Multispectral Satellite Imagery-The Regression Kriging Alternative.IEEE Geoscience and Remote Sensing Letters, 12(12):2511-2515.doi: 10.1109/LGRS.2015.2489678

Wang, J.J., Tian, Q.J., 2007.Study on Shallow Coastal Water Depth Derived Method Based on Hyperspectral Remote Sensing.Scientia Geographica Sinica, 27(6):843-848(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLKX200706019.htm

Wang, Y.J., Zhang, P.Q., Dong, W.J., et al., 2007.Study on Remote Sensing of Water Depths Based on BP Artifical Neural Network.Marine Science Bulletin, 9(1):26-35.

Yu, R.H., Xu, Y.P., Liu, T.X., et al., 2009.Reversing Water Depth in Shallow Lake of Arid Area Using Multi-Spectral Remote Sensing Information.Advances in Water Science, 20(1):111-117(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-SKXJ200901019.htm

Yu, Y.F., Yang, J.Z., Chen, S.B., et al., 2015.Lithologic Classification from Remote Sensing Images Based on Spectral Index.Earth Science, 40(8):1415-1419(in Chinese with English abstract). https://www.researchgate.net/publication/285601688_Lithologic...

Zhou, P., Li, N., Huo, H.Y., 2015.The Quality Assessment of Hymap Simulation Spaceborne Hyperspectral Data.Earth Science, 40(8):1310-1318(in Chinese with English abstract). http://d.wanfangdata.com.cn/Periodical_dqkx201508004.aspx

Zhu, Y., Zhao, Q., Zhou, X, D., 2013.Remote Sensing Water Depth Inversion Based on Chaotic Immune Optimization RBF Network.Computer Engineering, 39(5):187-191(in Chinese with English abstract). http://www.ecice06.com/EN/abstract/abstract24779.shtml

党福星, 丁谦, 2003.利用多波段卫星数据进行浅海水深反演方法研究.海洋通报, 22(3):55-60. http://www.cqvip.com/QK/92854X/200303/7858666.html

邓正栋, 叶欣, 关洪军, 等, 2013.基于RBF神经网络的水深遥感研究.解放军理工大学学报:自然科学版, 14(1):101-106. http://d.wanfangdata.com.cn/Periodical/jfjlgdxxb201301019

冯杭建, 周爱国, 俞建君, 等, 2016.浙西梅雨滑坡易发性评价模型对比.地球科学, 41(3):403-415. http://www.earth-science.net/WebPage/Article.aspx?id=3259

高国栋, 张文孝, 慕光宇, 2011.RBF网络和BP网络在海水盐度建模中的比较研究.海洋通报, 30(1):12-15. http://www.cqvip.com/QK/92854X/201101/37179382.html

高伟, 刘修国, 彭攀, 等, 2010.一种改进的高分辨率遥感影像分割方法.地球科学, 35(3):421-425. http://www.earth-science.net/WebPage/Article.aspx?id=1978

韩晓庆, 苏艺, 李静, 等, 2012.海岸带地区SPOT卫星影像大气校正方法比较及精度验证.地理研究, 31(11):2007-2016. http://www.oalib.com/paper/4252438

黄文骞, 吴迪, 杨杨, 等, 2013.浅海多光谱遥感水深反演技术.海洋技术, 32(2):43-46. http://www.cnki.com.cn/Article/CJFDTotal-HYJS201302011.htm

刘明堂, 张成才, 田壮壮, 等, 2015.基于RBF神经网络的黄河含沙量测量数据融合研究.水利水电技术, 46(1):126-130. doi: 10.3969/j.issn.1000-0860.2015.01.029

王晶晶, 田庆久, 2007.海岸带浅海水深高光谱遥感反演方法研究.地理科学, 27(6):843-848. http://www.oalib.com/paper/4168021

于瑞宏, 许有鹏, 刘廷玺, 等, 2009.应用多光谱遥感信息反演干旱区浅水湖泊水深.水科学进展, 20(1):111-117. http://www.wenkuxiazai.com/doc/a1fc38186529647d2628528f.html

于亚凤, 杨金中, 陈圣波, 等, 2015.基于光谱指数的遥感影像岩性分类.地球科学, 40(8):1415-1419. http://www.earth-science.net/WebPage/Article.aspx?id=3144

周萍, 李娜, 霍红元, 2015.基于Hymap模拟的星载高光谱数据质量评价.地球科学, 40(8):1310-1318. http://www.earth-science.net/WebPage/Article.aspx?id=3147

朱玉, 赵卿, 周兴东, 2013.基于混沌免疫优化RBF网络的遥感水深反演.计算机工程, 39(5):187-191. http://www.cqvip.com/QK/95200X/201305/45778853.html

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(10) / Tables(4)

Get Citation

PDF

XML

Article views (6655) PDF downloads(71)

Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network

doi: 10.3799/dqkx.2017.552

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content