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利用0-1矩阵分解集成的极化SAR图像分类

陈博 王爽 焦李成 刘芳 毛莎莎 张爽

陈博, 王爽, 焦李成, 刘芳, 毛莎莎, 张爽. 利用0-1矩阵分解集成的极化SAR图像分类[J]. 电子与信息学报, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059
引用本文: 陈博, 王爽, 焦李成, 刘芳, 毛莎莎, 张爽. 利用0-1矩阵分解集成的极化SAR图像分类[J]. 电子与信息学报, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059
Chen Bo, Wang Shuang, Jiao Li-cheng, Liu Fang, Mao Sha-sha, Zhang Shuang. Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059
Citation: Chen Bo, Wang Shuang, Jiao Li-cheng, Liu Fang, Mao Sha-sha, Zhang Shuang. Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059

利用0-1矩阵分解集成的极化SAR图像分类

doi: 10.11999/JEIT141059
基金项目: 

国家973计划项目(2013CB329402),国家自然科学基金(61271302, 61272282, 61202176, 61271298)和国家教育部博士点基金(20100203120005)资助课题

Polarimetric SAR Image Classification via Weighted Ensemble Based on 0-1 Matrix Decomposition

  • 摘要: 全极化合成孔径雷达(PolSAR)图像蕴含更丰富的散射信息,具有更多的可用特征。如何使用这些特征是极化SAR图像分类中非常重要的一步,但是目前尚未对此提出非常明确的准则。为了能够有效地解决上述问题,该文提出一种基于特征加权集成的极化SAR图像分类算法。该算法采用0-1矩阵分解集成方法对包括不同特征的数据集进行学习获得相应加权系数,并通过对每个特征集获得的预测结果进行加权集成来提高极化SAR图像分类性能。首先,输入极化SAR数据,获得极化特征作为原始特征集,并对其进行随机抽取获得不同的特征子集;然后,使用0-1矩阵集成算法得到每个特征值相对应的加权系数;最后,通过对各个特征子集的预测结果进行集成得到最终极化SAR图像分类结果。实测L波段和C波段极化数据的实验结果表明,该算法可以有效地提高极化SAR图像分类的准确度。
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出版历程
  • 收稿日期:  2014-08-11
  • 修回日期:  2014-10-22
  • 刊出日期:  2015-06-19

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