Automatic identification of irrigation and drainage system in land reclamation area based on object-oriented classification

被引：0
|
作者：
机构：
[1] Key Laboratory for Environmental and Urban Sciences, Shenzhen Graduate School, Peking University
[2] Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University
来源：
Liu, J. (ljz.401@163.com) | 1600年 / Chinese Society of Agricultural Engineering卷 / 28期
关键词：
Automation; Consolidation; High resolution remote sensing image; Image processing; Irrigation and drainage system; Land; Object-oriented classification;
D O I：
10.3969/j.issn.1002-6819.2012.08.004
中图分类号：
学科分类号：
摘要：
To identify irrigation and drainage system in land reclamation area automatically, an object-oriented classification method was proposed. The effectiveness of this method was compared with supervised classification method and manual screen digitization in terms of recognition accuracy and efficiency. KOMPSAT-2 high-resolution remote sensing images were selected as the experimental data, and the study area is located in Da'an city of western Jilin province. The experimental results showed that the overall recognition accuracy of object-oriented classification method was 89.64%, much higher than the accuracy of supervised classification method. More over, the object-oriented classification method is less time-consuming than manual screen digitization. The object-oriented classification method needs the least human intervention to complete the classification process and could achieve more stable results than the other two methods. Results show that the object-oriented classification is a powerful tool for remote sensing monitoring of irrigation and drainage system in land reclamation area. Meanwhile, this research provides an effective way for the identification of other ground objects in land reclamation projects.
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页码：25 / 31
页数：6
相关论文
共 34 条
  • [21] Benz U.C., Hofmann P., Willhauck G., Et al., Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, 58, 3-4, pp. 239-258, (2004)
  • [22] (2009)
  • [23] (2001)
  • [24] Zhang J., Li X., Wu Y., Object oriented estimation of winter wheat planting area using remote sensing data, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 24, 5, pp. 156-161, (2008)
  • [25] Vincent L., Soilie P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transaction on Pattern Analysis and Machine Intelligence, 13, 6, pp. 583-598, (1991)
  • [26] Chen Y., Feng T., Shi P., Et al., Classification of remot sensing image based on object oriented and class rules, Geomatics and Information Science of Wuhan University, 31, 4, pp. 316-320, (2006)
  • [27] (2004)
  • [28] Jin X.Y., Paswatcrs S., A fuzzy rule base system for object-based feature extraction and classification, Proceedings of the SPIE, 6567, (2007)
  • [29] Felkel P., Obdrzalek S., Straight skeleton implementation, Proceedings of the 14th Spring Conference on Computer Graphics, pp. 210-218, (1998)
  • [30] Tian Q., Luo Y., Hu D., Rounded straight skeleton and its implementation, Journal of Computer-Aided Design and Computer Graphics, 17, 12, pp. 2642-2646, (2005)
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