A maximum noise fraction transform with improved noise estimation for hyperspectral images

作者：Liu Xiang^*; Zhang Bing; Gao LianRu; Chen DongMei

来源：Science in China - Series F: Information Sciences , 2009, 52(9): 1578-1587.

DOI：10.1007/s11432-009-0156-z

• principal component transform
• maximum noise fraction transform
• hyperspectral image
• noise estimation

Feature extraction is often performed to reduce spectral dimension of hyperspectral images before image classification. The maximum noise fraction (MNF) transform is one of the most commonly used spectral feature extraction methods. The spectral features in several bands of hyperspectral images are submerged by the noise. The MNF transform is advantageous over the principle component (PC) transform because it takes the noise information in the spatial domain into consideration. However, the experiments described in this paper demonstrate that classification accuracy is greatly influenced by the MNF transform when the ground objects are mixed together. The underlying mechanism of it is revealed and analyzed by mathematical theory. In order to improve the performance of classification after feature extraction when ground objects are mixed in hyperspectral images, a new MNF transform, with an improved method of estimating hyperspectral image noise covariance matrix (NCM), is presented. This improved MNF transform is applied to both the simulated data and real data. The results show that compared with the classical MNF transform, this new method enhanced the ability of feature extraction and increased classification accuracy.

• 出版日期2009-9
• 单位中国科学院; 中国科学院遥感与数字地球研究所; 中国科学院对地观测与数字地球科学中心