Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

The important techniques in processing hyperspectral data acquired by interference imaging spectrometer onboard Small Satellite Constellation for Environment and Disaster mitigation (HJ-1A) are studied in this article. First, a new noise estimation method, named residualscaled local standard deviations, is used to analyze the noise condition of HJ-1A hyperspectral images. Then, an optimized maximum noise fraction (OMNF) transform is proposed for dimensionality reduction of HJ-1A images, which adopts an accurately estimated noise covariance matrix for noise whitening. The proposed OMNF method is less sensitive to noise distribution and interference existence, thus it can more efficiently compact useful data information in a low-dimensional space. The proposed OMNF is evaluated through two applications, i.e., spectral unmixing and classification, using the HJ-1A image acquired at the Bohai Sea area in China. It demonstrates that the proposed OMNF provides better performance in comparison with other traditional dimensionality reduction methods.