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Hyperspectral subspace identification
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DOI：10.1109/TGRS.2008.918089.png)
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Abstract
En 中文
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.
Keywords:
dimensionality reduction
hyperspectral imagery
hyperspectral signal subspace identification by minimum error (HySime)
hyperspectral unmixing
linear mixture
minimum mean square error (mse)
subspace identification
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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