Quantitative Reconstruction and Denoising Method HyBER for Hyperspectral Image Data and Its Application to CRISM

Abstract

We propose a new algorithm, hypothesis-based estimation with regularization (HyBER), to reconstruct and denoise hyperspectral image data without extra statistical assumptions. The hypothesis test selects the best statistical model approximating measurements based on the data only. A regularized maximum log-likelihood estimation method is derived based on the selected model. A spatially dependent weighting on the regularization penalty is presented, substantially eliminating row artifacts that are due to nonuniform sampling. A new spectral weighting penalty is introduced to suppress varying detector-related noise. HyBER generates reconstructions with sharpened images and spectra in which the noise is suppressed, whereas fine-scale mineral absorptions are preserved. The performance is quantitatively analyzed for simulations with 0.002% relative error, which is better than the traditional nonstatistical methods (baselines) and statistical methods with improper assumptions. When applied to the Mars Reconnaissance Orbiter's Compact Reconnaissance Imaging Spectrometer for Mars data, the spatial resolution and contrast are approximately two times better as compared to map projecting data without the use of HyBER.

Keywords

• Gaussian distributions
• image reconstruction
• maximum likelihood estimation
• noise
• Poisson distributions
• remote sensing