Rolling Guidance Based Scaled-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Spatial regularization based sparse unmixing has been attracted much attention and has achieved improved fractional abundance results. However, the traditional approach to spatial consideration can only suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. As the different levels of structures and edges in remote sensing have different meanings and importance, to better extract the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (RGSU), is proposed in this paper to extract and recover the different levels important structures and details in the hyperspectral remote sensing image unmixing procedure. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves a better performance and produces more accurate abundance maps, as well as higher quantitative results, when compared to the current state-of-the-art sparse unmixing algorithms