| 基于改进的像素级和对象级的遥感影像合成分类 |
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| 引用本文： | 李刚,万幼川.基于改进的像素级和对象级的遥感影像合成分类[J].测绘学报,2012,41(6):891-897. |
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| 作者姓名： | 李刚 万幼川 |
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| 作者单位： | 1. 武汉大学;
2. 武汉大学测绘遥感信息工程学院 |
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| 摘 要： | 像素级和对象级的分类研究分别作为两个独立的方向开展，二者的结合与优势互补还没有引起关注。本文对像素级和对象级分类方法的结合做出新的探索，提出了基于改进的像素级和对象级的遥感影像合成分类方法。首先，以一种改进的RBF神经网络分类器进行像素级分类、以一种基于改进模糊支持向量机和决策树的层次分类模型进行对象级分类，获得多层次分类结果。然后，提出了一个具体的像素级分类与对象级分类的合成算法，对多层次分类结果进行合成。实验表明，合成分类方法能有效地提高分类结果的精度，提供比单一像素级方法或对象级方法更准确的分类结果。
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| 关 键 词： | 像素级分类 对象级分类 合成分类 层次分类 |
| 收稿时间： | 2011-04-22 |
| 修稿时间： | 2012-05-09 |
Synthesis Classification of Remote Sensing Image Based on Improved Pixel-level and Object-level Methods |
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| LI Gang,WAN Youchuan.Synthesis Classification of Remote Sensing Image Based on Improved Pixel-level and Object-level Methods[J].Acta Geodaetica et Cartographica Sinica,2012,41(6):891-897. |
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| Authors: | LI Gang WAN Youchuan |
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| Institution: | School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China |
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| Abstract: | The pixel- and object-level classification methods are investigated separately, while the hybrid of them has not been explored. This paper tries new exploration on the hybrid of pixel- and object-level classification, and proposes synthesis classification method for remote sensing image based on improved pixel- and object-level classification. Firstly, an improved RBF neural network classifier is proposed to obtain the pixel-level classification result, and a hierarchy classification model based on improved fuzzy support vector machines and decision tree is utilized to obtain the object-level classification result. Then a specific synthesis algorithm of pixel- and object-level classification is proposed to obtain the synthesis classification result. The experiments show the synthesis classification method can improve the accuracy of classification result effectively and provide more accurate classification result than single pixel- or object-level method. |
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| Keywords: | pixel-level classification object-level classification synthesis classification hierarchy classification |
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