| Cite this article: | 张威.基于面向对象分类法的农田识别提取[J].灌溉排水学报,2019,(12):-. |
| zhangwei.基于面向对象分类法的农田识别提取[J].灌溉排水学报,2019,(12):-. |
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| Farmland Recognition and Extraction Based on Object-oriented Classification |
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zhangwei
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College of Geography and Tourism, Chongqing Normal University
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| Abstract: |
| 【Objective】In order to provide a method for accurately obtaining farmland classification results at a county scale using medium and low resolution remote sensing images.【Method】Taking medium-resolution landsat8 OLI remote sensing image data as data source and using the object-oriented CART decision tree classification method to identify and extract the farmland in Dianjiang County, then comparison the classification results with maximum likelihood method which extract based on pixel.【Result】The research results showed that: (1) Compared with the maximum likelihood classification method, the CART decision tree classification method has higher precision, the overall classification accuracy and Kappa coefficient reached 88.8% and 0.85, respectively, and the mapping accuracy for dry land and paddy fields is over 90%; (2) In the determination of the segmentation scale, the ESP tool in the eCognition software can quickly determine the optimal segmentation scale and improving efficiency and scientificity; (3) On the county scale, the object-oriented classification method also has certain applicability to remote sensing extraction of medium resolution image data.【Conclusion】The object-oriented classification method based on landsat8 OLI remote sensing data can realize the need of low-cost and high-precision farmland classification at county scale, and also provides a reference for mitigating the contradiction between precision and cost, spatial resolution and extraction method. |
| Key words: remote sensing; object-oriented classification; supervised classification; county scale; farmland extraction |
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