Hyperspectral image analysis using Noise-Adjusted Principal Component transform

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.