Spectral-Spatial Anti-Interference NMF for Hyperspectral Unmixing

Hyperspectral unmixing could provide decomposition for small units in hyperspectral image, allowing accurate analysis of ground objects. Unfortunately, interference such as noise and spectral variability prevalent in hyperspectral data poses a serious challenge for it. Accordingly, this paper proposes a spectral-spatial anti-interference nonnegative matrix factorization (NMF) algorithm (SSAINMF), which improves the performance of spectral unmixing from both spectral and spatial perspectives. Specifically, the original data is analyzed and transformed into a statistical domain where the information of each dimension can be re-expressed, followed by a proof of restricted isometric and restricted isospectral properties for endmembers and abundances between the original domain and the transformation domain. To obtain more reliable endmembers, weighting is then applied to each dimension in the transformation domain depending on the priority coefficients quantified by their contribution to data representation, with the influence of anomalous and noisy data weakened and the priorities of low-rank information emphasized. Finally, superpixels are exploited to induce local similarity and structural sparsity of abundances within the neighborhood, which reduces the sensitivity to spatial noise and spectral variability. From experimental results on synthetic and real data sets, the proposed SSAINMF has demonstrated effectiveness in decomposing mixed pixels, with better robustness.