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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Hyperspectral Image Analysis Using Noise-Adjusted Principal Component Transform
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Hyperspectral Image Analysis Using Noise-Adjusted Principal Component Transform

机译：使用噪声调整后的主成分变换进行高光谱图像分析

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The Noise-Adjusted Principal Components (NAPC) transform, or Maximum Noise Fraction (MNF) transform, has received considerable interest in the remote sensing community. Its basic idea is to reorganize the data such that the principal components are ordered in terms of signal to noise ratio (SNR), instead of variance as used in the ordinary principal components analysis (PCA). The NAPC transform is very useful in multi-dimensional image analysis, because SNR is directly related to image quality. As a result, object information can be better compacted into the first several principal components. This paper reviews the fundamental concept of the NAPC transform and its practical implementation issue, i.e., how to get accurate noise estimation, the key to the success of its implementation. Three applications of the NAPC transform in hyperspectral image analysis are presented, which are image classification, image compression, and image visualization. The AVIRIS data is used for demonstration, which shows that using the NAPC transform the performance of the following data analysis can be significantly improved because of more informative major principal components.
机译：噪声调整主分量（NAPC）变换或最大噪声分数（MNF）变换在遥感界引起了极大的兴趣。它的基本思想是重新组织数据，使主成分按信噪比（SNR）排序，而不是像普通主成分分析（PCA）中使用的方差那样进行排序。由于SNR与图像质量直接相关，因此NAPC变换在多维图像分析中非常有用。结果，可以将对象信息更好地压缩为前几个主要组件。本文回顾了NAPC变换的基本概念及其实际实现问题，即如何获得准确的噪声估计，这是实现成功的关键。介绍了NAPC变换在高光谱图像分析中的三个应用，分别是图像分类，图像压缩和图像可视化。 AVIRIS数据用于演示，这表明使用NAPC变换可以大大提高后续数据分析的性能，因为主要信息量更大。

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