Ieee Geoscience and Remote Sensing Letters 1 Regularization Framework for Target Detection in Hyperspectral Imagery
Ieee Geoscience and Remote Sensing Letters 1 Regularization Framework for Target Detection in Hyperspectral Imagery
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DOI：
10.1109/lgrs.2013.2257666
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发表时间：
2014
影响因子：
4.8
通讯作者：
Yuxiang Zhang;Bo Du;Liangpei Zhang
中科院分区：
文献类型：
--
作者：
Yuxiang Zhang;Bo Du;Liangpei Zhang
高光谱图像中的目标检测是信号处理领域的研究热点。目标检测方法的关键在于正确估计由HSI估计出的各种测量矩阵。然而，这些矩阵通常是病态的，由于高维的HSI或不同的图像波段之间的固有相关性，这可能会导致不准确的逆矩阵估计。因此，如何处理可能不准确的逆计算极大地影响了检测性能。这封信提出了一个正则化框架，适用于目标检测器中使用的最先进的测量矩阵。该算法通过在这些矩阵上增加一个缩放单位矩阵来增强逆矩阵的稳定性，从而提高检测性能。在HSI上进行了大量的实验，结果表明正则化检测器的性能明显优于原始检测器。
Target detection in hyperspectral imagery (HSI) is of great interest in the signal processing field. The key to the target detection methods lies in the proper estimation of a variety of measurement matrices that are estimated from the HSI. However, these matrices are usually ill conditioned due to the high dimension of the HSI or the inherent correlation between different image bands, which can result in inaccurate inverse matrices estimation. Therefore, how to handle the potentially inaccurate inverse calculation greatly affects the detection performance. This letter proposes a regularization framework that is suitable for the state-of-the-art measurement matrices used in target detectors. It adds a scaled identity matrix to these matrices in order to strengthen the stability of the inverse matrices, and, consequently, improve the detection performance. Extensive experiments were carried out on HSI that revealed the regularized detectors clearly outperformed the original detectors.