| 高光谱图像高维多尺度自回归有监督检测 |
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| 引用本文： | 贺霖,潘泉,邸韡,李远清.高光谱图像高维多尺度自回归有监督检测[J].自动化学报,2009,35(5):509-518. |
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| 作者姓名： | 贺霖 潘泉 邸韡 李远清 |
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| 作者单位： | 1.华南理工大学自动化科学与工程学院 广州 510640 |
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| 基金项目： | 国家自然科学基金重点项目，国家自然科学基金，航空科学基金，武器装备预研基金，教育部新世纪人才计划项目，教育部高等学校博士学科点专项科研基金，广东省自然科学基金团队项目 |
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| 摘 要： | 给出一种有监督检测算法以检测高光谱图像中的区域目标. 为利用高光谱图像中的空间尺度维信息, 在高光谱图像多尺度观测不同相连节点之间建立高维多尺度自回归模型, 并利用四叉树节点间的多阶马尔可夫性和高维多尺度回归噪声先验概率密度与高维观测条件概率密度的等价性及其多元 t 分布特性, 构造出适用于检测高光谱图像中区域目标的空间多尺度自回归有监督检测算法. 理论分析及实验中的5种评价方法的结果均表明该检测器可有效检测出高光谱图像中的目标区域.
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| 关 键 词： | 高光谱图像 高维多尺度自回归 有监督检测 区域目标 |
| 收稿时间： | 2007-7-9 |
| 修稿时间： | 2008-5-6 |
Supervised Detection for Hyperspectral Imagery Based on High-dimensional Multiscale Autoregression |
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| HE Lin PAN Quan DI Wei LI Yuan-Qing. College of Automation Science , Engineering,South China University of Technology,Guangzhou .College of Automation,Northwestern Polytechnical University,Xi'an . Laboratory for Applications of Remote Sensing,Purdue University,West Lafayette,IN,USA -.Supervised Detection for Hyperspectral Imagery Based on High-dimensional Multiscale Autoregression[J].Acta Automatica Sinica,2009,35(5):509-518. |
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| Authors: | HE Lin PAN Quan DI Wei LI Yuan-Qing College of Automation Science Engineering South China University of Technology Guangzhou College of Automation Northwestern Polytechnical University Xi'an Laboratory for Applications of Remote Sensing Purdue University West Lafayette IN USA - |
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| Affiliation: | 1.College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640;2.College of Automation, Northwestern Polytechnical University, Xi'an 710072;3.Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA 47907-2045 |
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| Abstract: | A supervised detection algorithm is presented to detect the target region in hyperspectral imagery. In order to utilize the spatial scale information in hyperspectral data, the multiscale observation of hyperspectral imagery of different connected nodes at different scales are described by a high-dimensional autoregressive model. Then, a high-dimensional multiscale autoregression based detector to detect target region is constructed, utilizing the equality between joint distribution of various multiscale observations and that of the regression noise, and the multivariate t distribution statistics of the regression noise. Theoretical analysis and the experiment involving five performance indexes show that our detector is effective to detect target region in hyperspectral imagery. |
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| Keywords: | Hyperspectral imagery high-dimensional multiscale autoregression supervised detection region target |
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