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- W2567584640 abstract "This paper proposes an efficient paddy field mapping method using object-based image analysis and a bitemporal data set acquired by Landsat-8 Operational Land Imager. In the proposed approach, image segmentation is the first step and its quality has a serious impact on the accuracy of paddy field classification. In order to improve segmentation quality, a new segmentation algorithm based on a frequently used method, fractal net evolution approach, is developed, with improvement mainly in merging criteria. In order to automate the process of scale parameter determination, an unsupervised scale selection method is utilized to determine the optimal scale parameter for the proposed image segmentation approach. After segmentation, four types of object-based features including geometric, spectral, textural, and contextual information are extracted and input into the subsequent classification procedure. By using a random forest classifier, paddy fields and nonpaddy fields are separated. The proposed image segmen..." @default.
- W2567584640 created "2017-01-06" @default.
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- W2567584640 date "2016-12-29" @default.
- W2567584640 modified "2023-10-12" @default.
- W2567584640 title "Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach" @default.
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- W2567584640 doi "https://doi.org/10.1080/15481603.2016.1273438" @default.
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