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基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法

许宁 尤红建 耿修瑞 曹银贵

许宁, 尤红建, 耿修瑞, 曹银贵. 基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法[J]. 电子与信息学报, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011
引用本文: 许宁, 尤红建, 耿修瑞, 曹银贵. 基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法[J]. 电子与信息学报, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011
XU Ning, YOU Hongjian, GENG Xiurui, CAO Yingui. Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery[J]. Journal of Electronics and Information Technology, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011
Citation: XU Ning, YOU Hongjian, GENG Xiurui, CAO Yingui. Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery[J]. Journal of Electronics and Information Technology, 2016, 38(11): 2701-2708. doi: 10.11999/JEIT160011

基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法

doi: 10.11999/JEIT160011
基金项目: 

中国地质调查局地质调查项目(1212011120226),国家863计划(2012AA12A308),中国科学院科技服务网络计划项目(KFJ- EW-STS-046)

Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery

Funds: 

The Geological Survey Program of China Geological Survey (1212011120226), The National 863 Program of China (2012AA12A308), The Science and Technology Services Network Program of Chinese Academy of Sciences (KFJ-EW- STS-046)

  • 摘要: 基于图像中存在的邻域以及非局部相似等图像空间特征和联合稀疏解混思想,该文提出一种基于高光谱图像光谱相似性度量的多任务联合稀疏解混方法。通过高光谱图像的光谱特性统计值设定光谱度量阈值,对高光谱图像中相似的像元光谱进行光谱相似性度量分组,再对分组像元光谱数据进行多任务联合稀疏光谱解混模型的构建和求解,得到最终的丰度系数。模拟数据实验结果表明,该方法一定程度上提升了现有联合稀疏光谱解混方法的丰度估计精度,真实数据结果也验证了方法的有效性。
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    出版历程
    • 收稿日期:  2016-01-04
    • 修回日期:  2016-06-06
    • 刊出日期:  2016-11-19

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