Abstract
Remote sensing image classification is an important technology to get information. At present, different remote sensing monitoring methods has been widely used in region land cover. To improve classification accuracy is the key of remote sensing data processing and application. This paper selects Xingyun Lake that the typical Plateau Lake area of Yunnan province and the surrounding lakeside zone as research area. Based on the 30 TM Landsat remote sensing image of the research area, using supervised classification, BP neural network, and object-oriented classification to compare accuracy of three kinds of classification methods. It was found that development of BP neural network and object-oriented classification training produces more accurate results than supervised training. Object-oriented classification also produced more accurate classification than the BP neural network classification, but did not improve the accuracy significantly. The results will help to promote surface coverage information of remote sensing rapidly extraction and dynamic monitoring in the Yunnan plateau lake, moreover, it has important scientific significance to protect and formulate rationalization.
Thanks to the support of National Natural Science Foundation of China (No. 41561083, No. 41261092) and Natural Science Foundation of Yunnan Province (2015FA016).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jia, K., Li, Q., Tian, Y., Wu, B.: A review of classification methods of remote sensing imagery. Spectrosc. Spectral Anal. 10, 2618–2623 (2011)
Yingshi, Z.: The Principle and Method of Analysis of Remote Sensing Application. Science Press, Beijing (2003)
Foody, G.M.: Int. J. Remote Sens. 17, 1317 (1996)
Bolstad, P.V., Gessler, P., Lillesand, T.M.: Positional uncertainty in manually digitized map data. Int. J. Geog. Inf. Syst. 4, 399 (1990)
Chengcai, Z., Xiaonan, D., Nan, Z., Ying, Z.: Comparative study of the remote sensing image classification method based on water area estimation. Meteorol. Environ. Sci. 03, 24–28 (2008)
Yin, Z., Du, P.: Study on object-oriented image classification for hyper spectral remote sensing. Remote Sens. Inf. 04, 29–32 (2007)
Mai, G., Tong, X.: Study of BP neural network in rocky desertification remote sensing image classification method. J. Guangxi Teach. Educ. Univ.: Nat. Sci. Ed. 03, 70–77 (2013)
Karnieli, A.: Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Appl. Geogr. 31(2), 533–544 (2011)
Jing, Y., Yongfeng, C.: Accuracy evaluation of the RadarSat-2 full polari metric data for land cover classification. Comput. Telecommun. (1), 18–20 (2015)
Yinhui, Z., Gengxing, Z.: Classification methods of land use cover based on remote sensing technologies. J. China Agric. Resour. Reg. Plan. 03, 24–28 (2002)
Zhong, C.: Research on High Resolution Remote Sensing Image Classification Technology. Chinese Academy of Sciences, Beijing (2006)
Cao, B., Qin, Q., Ma, H., Qiu, Y.: Research on spatial variability of water quality parameter and their adequate sampling amount in meiliang bayou of taihu lake. Geogr. Geo-Inf. Sci. 02, 46–49+54 (2006)
Wang, C., Wu, W., Zhang, J.: Classification for remote sensing image based on BP neural network. J. Liaoning Tech. Univ. (Nat. Sci.) 01, 32–35 (2009)
Lu, L., Zhang, Q., Li, G.: Image classification of remote sensing based on BP neural networks. Sci. Surv. Mapp. 06, 140–143 (2012)
Acknowledgments
Our sincere thanks to Nature Science Foundation of China (NSFC) (Nos. 41561083, 41261092) and Natural Science Fund of Yunnan Province (No. 2015FA016) for providing funding to carry put the research at Kunming University of Science and Technology, China. The authors would like to thank two anonymous reviewers for their constructive comments which were helpful to bring the manuscript into its current form.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, C., Gan, S., Yi, D., Wu, Y. (2017). Comparison of Different Remote Sensing Monitoring Methods for Land-Use Classification in Yunnan Plateau Lake Area. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-3969-0_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3968-3
Online ISBN: 978-981-10-3969-0
eBook Packages: Computer ScienceComputer Science (R0)