Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory
Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory
复制标题
使用随机矩阵理论确定高光谱图像的内在维度
DOI：
10.1109/tip.2012.2227765
复制
发表时间：
2013
影响因子：
10.6
通讯作者：
M. Sears
中科院分区：
文献类型：
--
作者：
K. Cawse‐Nicholson;S. Damelin;A. Robin;M. Sears
确定高光谱图像的固有维度是光谱解混过程中的重要步骤，并且在无监督方法中，对该数字的低估或高估可能导致不正确的解混。在本文中，我们讨论了一种新的方法来确定的内在维数使用随机矩阵理论的最新进展。该方法完全无监督，不受任何用户确定的参数的影响，并允许数据中存在光谱相关噪声。对合成数据进行稳健性测试，以确定结果如何受到噪声水平、噪声变异性、噪声近似和端元光谱特性的影响。成功率被确定为许多不同的合成图像，和该方法进行了测试，对两对真实的图像，即从机载可见光红外成像光谱仪（AVIRIS）和SpecTIR传感器拍摄的赤铜矿场景，和从AVIRIS和月球的湖泊场景，具有良好的效果。
Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process and under- or overestimation of this number may lead to incorrect unmixing in unsupervised methods. In this paper, we discuss a new method for determining the intrinsic dimension using recent advances in random matrix theory. This method is entirely unsupervised, free from any user-determined parameters and allows spectrally correlated noise in the data. Robustness tests are run on synthetic data, to determine how the results were affected by noise levels, noise variability, noise approximation, and spectral characteristics of the end-members. Success rates are determined for many different synthetic images, and the method is tested on two pairs of real images, namely a Cuprite scene taken from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and SpecTIR sensors, and a Lunar Lakes scene taken from AVIRIS and Hyperion, with good results.