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| DOI：10.13522/j.cnki.ggps.2019027 |
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| Farmland Recognition and Extraction Based on Object-oriented Classification |
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ZHANG Wei, LIU Yi, SHAO Jingan
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1. College of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China;2. Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area,
Chongqing Normal University, Chongqing 401331, China
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| Abstract: |
| 【Objective】The purpose of this paper is to provide a method for accurately obtaining farmland classification results at a county scale using medium and low resolution remote sensing images.【Method】The medium-resolution landsat8 OLI remote sensing image data was taker as data source and using the object-oriented CART decision tree classification method was used identify and extract the farmland in Dianjiang County, then comparison the classification results with maximum likelihood method which extract based on pixel.【Result】①Compared with the maximum likelihood classification method, the CART decision tree classification method had 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 was over 90%; ② In the determination of the segmentation scale, the ESP tool in the eCognition software could 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 provided 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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