Spectral unmixing using Lasso screening rules

Lasso (Least Absolute Shrinkage and Selection Operator) is a technique for selecting a sparse combination of given features. Spectral unmixing is a Lasso problem, regarding that the spectrum of every pixel is a linear combination of a small number of spectrums from a possibly very large spectral library. In this paper we apply a technique called screening to speedup the Lasso process for spectral unmixing. Our contribution is two-fold: we make use of the theoretical results to practical remote sensing problems; more importantly, we develop a tailored Lasso algorithm coupled with screening, as the unmixing requires that the fractions should be positive and sum to one. We also solve the high mutual coherence problem in the library by ticking out the spectrums with high mutual coherence. We use the AVIRIS data over Cuprite, Nevada (250 lines by 191 columns) to demonstrate the idea. Experiments demonstrate the effectiveness of the screening method.