Dual Spatial Weighted Sparse Hyperspectral Unmixing

Abstract

Sparse unmixing is a semi-supervised method whose pur-pose is to find the best subset of library entries from the spec-tral library that best model the image. In sparse unmixing, the current main development direction is to incorporate the spatial information of the image into the model. Existing spa-tial sparse unmixing algorithms mainly use spatial weights or spatial regularization to characterize the spatial correlation between pixels to improve the unmixing results. For the complex and diverse hyperspectral data in reality, most al-gorithms are only good at processing a single scene, which brings greater challenges to their practicality. In order to ad-dress this issue, a new dual spatial weighted sparse unmixing model (DSWSU) is proposed, which simultaneously ex-ploits the spatially homogeneous information of images. For the proposed DSWSU, a pre-calculated superpixel weighting factor is designed to mitigate the effect of noise on unmixing. Meanwhile, the spatial neighborhood weighting factor aims to promote the local smoothness of the abundance maps. As a simple unmixing model, the proposed DSWSU can be quickly solved by the alternating direction multiplier method (ADMM). Experimental results on simulated hyperspectral data indicate that the proposed DSWSU method can achieve accurate abundance estimation in various scenarios (low or high noise interference), and obtain better unmixing results than other state-of-the-art unmixing algorithms.