%0 Journal Article
%T Supervised Detection for Hyperspectral Imagery Based on High-dimensional Multiscale Autoregression<br>高光谱图像高维多尺度自回归有监督检测
%A HE Lin PAN Quan DI Wei LI Yuan-Qing College of Automation Science
%A Engineering
%A South China University of Technology
%A Guangzhou College of Automation
%A Northwestern Polytechnical University
%A Xi'an Laboratory for Applications of Remote Sensing
%A Purdue University
%A West Lafayette
%A IN
%A USA -
%A <br>贺霖
%A 潘泉
%A 邸韡
%A 李远清
%J 自动化学报
%D 2009
%I 
%X 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.
%K Hyperspectral imagery
%K high-dimensional multiscale autoregression
%K supervised detection
%K region target<br>高光谱图像
%K 高维多尺度自回归
%K 有监督检测
%K 区域目标
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=A24BBD4CD3E2CFB583F16B3B40D7D9E1&yid=DE12191FBD62783C&vid=6209D9E8050195F5&iid=94C357A881DFC066&sid=4ECB3941871FD391&eid=AE43DE0664B02889&journal_id=0254-4156&journal_name=自动化学报&referenced_num=1&reference_num=0