| 基于资源一号02C高分辨率数据的农业区土地利用分类 |
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| 引用本文： | 林 楠,姜琦刚,杨佳佳,陈永良,马 玥.基于资源一号02C高分辨率数据的农业区土地利用分类[J].农业机械学报,2015,46(1):278-284. |
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| 作者姓名： | 林 楠 姜琦刚 杨佳佳 陈永良 马 玥 |
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| 作者单位： | 吉林大学;吉林建筑大学,吉林大学,沈阳地质调查中心,吉林大学,吉林大学 |
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| 基金项目： | 国家自然科学基金资助项目（41072244、41272360）和中国地质调查局资助项目（1212011220105） |
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| 摘 要： | 利用最小二乘支持向量机良好的非线性划分能力,基于资源一号02C高分辨率遥感数据,结合图像形状、纹理特征等信息,对农业区土地利用类型进行快速分类提取,结果表明:资源一号02C高分辨率数据可以快速有效地实现土地类型划分,加入特征信息后的图像分类精度大幅度提高,而最小二乘支持向量机的分类结果也十分理想,总体分类精度达到82.53%,Kappa系数达到0.807 1,高于传统图像分类方法,为利用国产高分辨率卫星进行土地类型划分提供了快速可行的方法。
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| 关 键 词： | 农业区 土地利用 资源一号02C 最小二乘支持向量机 高分辨率遥感 |
| 收稿时间： | 2014/1/26 0:00:00 |
Classifications of Agricultural Land Use Based on High-spatial Resolution ZY1-02C Remote Sensing Images |
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| Lin Nan,Jiang Qigang,Yang Jiaji,Chen Yongliang and Ma Yue.Classifications of Agricultural Land Use Based on High-spatial Resolution ZY1-02C Remote Sensing Images[J].Transactions of the Chinese Society of Agricultural Machinery,2015,46(1):278-284. |
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| Authors: | Lin Nan Jiang Qigang Yang Jiaji Chen Yongliang and Ma Yue |
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| Institution: | Jilin University;Jilin Jianzhu University,Jilin University,Shenyang Institute of Geology and Mineral Resources,Jilin University and Jilin University |
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| Abstract: | Applying the good nonlinear classification ability of the least squares support vector machine (SVM) algorithm, this paper conduced the classification of land use in agricultural district from the high-spatial resolution ZY1-02C remote sensing images, which was based on the SVM method integrating information of shape and texture. It shows that the high-spatial resolution ZY1-02C data can realize land classification quickly and effectively, and the classification accuracy is increased by adding the feature information. The least squares SVM classification results were ideal, the overall accuracy was 82.53%, and the Kappa coefficient was 0.8071. It has higher accuracy than traditional method and provides a feasible method for the classification of land use based on domestic high-spatial resolution satellite. |
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| Keywords: | Agricultural land Land use ZY1-02C Least square support vector machine High spatial resolution |
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