Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery
Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery
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DOI：
10.1109/lsp.2008.917805
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发表时间：
2008-03
影响因子：
3.9
通讯作者：
N. Nasrabadi
中科院分区：
文献类型：
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作者：
N. Nasrabadi
这封信将正则化的想法扩展到光谱匹配滤波器。它在光谱匹配滤波器的设计中结合了二次惩罚项，以便将可能的匹配滤波器（模型）限制为比非正则化自适应光谱匹配滤波器更稳定且具有更好性能的子集。正则化的效果取决于正则化项的形式以及由称为正则化系数的参数控制的正则化量。在这封信中，滤波器系数的平方和被用作正则化项，并且测试了正则化系数的不同值。还描述了基于贝叶斯的正则化匹配滤波器的推导，其提供了选择正则化系数的过程。给出了正则化和非正则化光谱匹配滤波器在高光谱图像中检测目标的实验结果。
This letter extends the idea of regularization to spectral matched filters. It incorporates a quadratic penalization term in the design of spectral matched filters in order to restrict the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization which is controlled by a parameter so-called the regularization coefficient. In this letter, the sum-of-squares of the filter coefficients is used as the regularization term, and different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also described which provides a procedure for choosing the regularization coefficient. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.