Noise reduction of hyperspectral data using singular spectral analysis

In this study, a new noise reduction algorithm based on singular spectral analysis (SSA) was developed to reduce the noise in hyperspectral data. With this SSA-based approach, the reflectance spectrum of a given pixel in a hyperspectral cube is transformed into its state space. The state space is dynamically constructed and characterized by irregular bases, which allows the proposed approach to reduce noises while keeping the absorption features of surface objects. The performance of the developed method was verified on three datasets: two simulated reflectance spectra with several narrow absorption features and a CHRIS (Compact High Resolution Imaging Spectrometer) data cube over agricultural fields. Our results demonstrated the effectiveness of the SSA-based approach in improving the signal-to-noise ratio of hyperspectral data, while keeping the 'sharp features' in the reflectance spectra. The results also show that the proposed SSA method outperforms the commonly used MNF (minimum noise fraction) and wavelet-based noise reduction methods and it improved vegetation cover classification accuracy by 6%.