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Hyperspectral Subspace Identification Using SURE
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AI
DOI：10.1109/LGRS.2015.2485999.png)
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Abstract
En 中文
The identification of the signal subspace is a very important first step for most hyperspectral algorithms. In this letter, we investigate the important problem of identifying the hyperspectral signal subspace by minimizing the mean squared error (MSE) between the true signal and an estimate of the signal. Since it is dependent on the true signal, the MSE is uncomputable in practice, and so we propose a method based on Stein's unbiased risk estimator that provides an unbiased estimate of the MSE. The resulting method is simple and fully automatic, and we evaluate it using both simulated and real hyperspectral data sets. Experimental results show that our proposed method compares well to recent state-of-the-art subspace identification methods.
Keywords:
Hyperspectral imaging (HSI)
mean squared error (MSE)
model selection
rank selection
Stein's unbiased risk estimator (SURE)
tuning parameter selection
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K