Adaptive Total Variation Regularized for Hyperspectral Unmixing

Abstract

The purposed of hyperspectral unmixing is to estimate the spectral signatures composing the data (endmembers) and their abundance fractions. However, most of the traditional sparse unmixing methods are effective in the case of high signal-to-noise ratio (SNR), but is not good in the case of high noise. In order to solve this problem, we innovatively integrates adaptive total variation (ATV) regularization into hyperspectral sparse unmixing and propose a new hyperspectal sparse unmixing model named adaptive total variation regularized for sparse unmixing (SU_ATV). The model can adaptively adjust the horizontal difference and vertical difference of TV, can better optimize the efficiency of TV to improve the anti-noise performance. The experimental results show that SU_ATV has good anti-noise performance to the sparse unmixing.