A Maximum Noise Fraction Transform Based on a Sensor Noise Model for Hyperspectral Data

The maximum noise fraction (MNF) transform, which produces the improved order of components by signal to noise ratio (SNR), has been commonly used for spectral feature extraction from hyperspectral remote sensing data before image classification. When hyperspectral data contains a spectral distortion, also known as a “smile” property, the first component of the MNF, which should have high image quality, suffers from noisy brightness gradient pattern which thus reduces classification accuracy. This is probably because the classic noise estimation of the MNF is different from the real noise model. The noise estimation is the most important procedure because the noise covariance matrix determines the characteristics of the MNF transform. An improved noise estimation method from a single image based on a noise model of a charge coupled device (CCD) sensor is introduced to enhance the feature extraction performance of the MNF. This method is applied to both airborne and spaceborne hyperspectral data, acquired from the airborne visible infrared/imaging spectrometer (AVIRIS) and the EO-1/Hyperion, respectively. The experiment for the Hyperion data demonstrates that the proposed MNF is resistant to the spectral distortion of hyperspectral data. Furthermore, the image classification experiment for the AVIRIS Indian pines data using the MNF as a preprocessing step to extract spectral features shows that the proposed method extracts higher SNR components in lower MNF components than the existing feature extraction methods.