| 高分辨率遥感影像的支持张量机分类方法 |
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| 引用本文： | 张乐飞,黄昕,张良培.高分辨率遥感影像的支持张量机分类方法[J].武汉大学学报(信息科学版),2012,37(3):314-317. |
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| 作者姓名： | 张乐飞 黄昕 张良培 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室,武汉市珞喻路129号,430079 |
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| 基金项目： | 国家自然科学基金资助项目，中央高校基本科研业务费专项资金资助项目，测绘遥感信息工程国家重点实验室专项科研经费资助项目 |
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| 摘 要： | 针对高分辨率遥感数据分类多特征、小样本的特点,将训练样本像素邻域的数据立方以三阶张量表征,并提出了利用支持张量机对训练样本进行监督分类的模型和解法。实验结果表明,此方法能够利用少量的训练样本实现更优的分类精度。
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| 关 键 词： | 多特征 支持张量机 分类 |
Classification of High Spatial Resolution Imagery Using Support Tensor Machine |
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| ZHANG Lefei,HUANG Xin,ZHANG Liangpei.Classification of High Spatial Resolution Imagery Using Support Tensor Machine[J].Geomatics and Information Science of Wuhan University,2012,37(3):314-317. |
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| Authors: | ZHANG Lefei HUANG Xin ZHANG Liangpei |
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| Institution: | 1(1 State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing, Wuhan University,129 Luoyu Road,Wuhan 430079,China) |
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| Abstract: | We propose a support tensor machine for remote sensing image classification.The training samples are represented as 3-order tensors with local neighbor information.Then the mathematical model and solution of support tensor machine are discussed in detail.A range of experiments demonstrate that the effectiveness of the proposed method can deliver a high classification rate with a small number of training samples. |
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| Keywords: | multi-feature support tensor machine classification |
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