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surrogates

于 2020-09-09 发布文件大小:479KB

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下载积分: 1 下载次数: 131

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代码说明：

替代数据法中最重要的文献和文献相关的软件代码，非常经典亚(alternative method of data the most important literature and the literature related to the software code, very classic Asia)

文件列表：

sdarticle.pdf
surrogates
..........\surrogates
..........\..........\comp_rev.m
..........\..........\end2end.m
..........\..........\generate_AAFT.m
..........\..........\generate_anneal.m
..........\..........\generate_AR.m
..........\..........\generate_ar_signal.m
..........\..........\generate_ciAAFT.m
..........\..........\generate_corr_pair.m
..........\..........\generate_FT.m
..........\..........\generate_henon.m
..........\..........\generate_iAAFT.m
..........\..........\generate_iAAFTn.m
..........\..........\generate_randperm.m
..........\..........\generate_stat_boot.m
..........\..........\index.txt
..........\..........\minmin.m
..........\..........\quick_tutorial.txt
..........\..........\ranktest.m

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Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

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2010-10-13 03:51:05下载

积分：1

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