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?Local One Dimensional Embedding for Hyperspectral Image Classification
编辑:      澳门新葡新京:2018-12-05       点击数:
报告时间 2018年12月8日10:00 报告地点 澳门新葡新京201学术报告厅
报告人 李红(华中科技大学)

报告名称:Local One Dimensional Embedding for Hyperspectral Image Classification

主办单位:澳门新葡新京

报告专家:李红

专家所在单位:华中科技大学

报告时间:2018年12月8日(周日)上午10:00

报告地点:澳门新葡新京201报告厅

专家概况:李红,教授,博士生导师,科技部国际科技合作计划评议专家,湖北省计算数学学会理事,美国IEEE会员。2006年至2017年期间多次应邀访问香港浸会大学、澳门大学、美国加州大学尔湾分校(UCI)、澳大利亚悉尼大学等;主要研究兴趣:逼近与计算、机器学习与图像处理、学习理论与模式识别等领域。十余次出席国际学术会议,发表学术论文50余篇;主持国家自然科学基金、国防预研基金等项目20余项。2006年获宝钢教育基金“优秀教师”奖;2009年所主持的“复变函数与积分变换”课程评为国家精品课程、2016年评为第一批国家精品资源共享课;2013年获湖北省“教学研究成果二等奖”;2014年获“湖北名师奖”。

报告摘要:In hyperspectralimage (HSI) classification, the combination of spectral information and spatial information can be applied to enhance the classification performance. In order to better characterize the variability of spatial features at different scales, we propose a new framework called multiscale spatial information fusion (MSIF). The MSIF consists of three parts: multiscale spatial information extraction, local 1-D embedding (L1-DE), and information fusion. First, spatial filter with different scales is used to extract multiscale spatial information. Then L1-DE is utilized to map the spectral information and spatial information at different scales into 1-D space, respectively. Finally, the obtained 1-D coordinates are used to label the unlabeled spatial neighbors of the labeled samples. The proposed MSIF captures intrinsic spatial information contained in homogeneous regions of different sizes by multiscale strategy. Since the spatial information at different scales is processed separately in MSIF, the variance of spatial information at different scales can be reflected. The use of L1-DE reduces computational cost by mapping high-dimensional samples into 1-D space. In MSIF, the L1-DE and information fusion are used iteratively, and the iterative process terminates in a finite number of steps. The algorithm analysis demonstrates the effectiveness of the proposed method. The experimental results on four widely used HSI data sets show that the proposed method achieved higher classification accuracies compared with other state-of-the-art spectral-spatial classification methods.


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