Hyperspectral unmixing aims to correctly estimate the endmembers and their corresponding abundance fractions in an HSI. Many hyperspectral unmixing methods have been proposed, including the longstanding geometry-based, statistics-based and non-negative matrix factorization (NMF)-based unmixing methods. The traditional NMF-based method expands the three-dimensional hyperspectral data into matrix form and decomposes it into the product of the endmember and the abundance, which causes a certain degree of information loss. The matrix-vector nonnegative tensor factorization algorithm solves this problem well by processing hyper-spectral data as a tensor and pioneers a new model based on tensor decomposition. However, such methods still suffer from underutilization of image information and unstable performance at low signal-to-noise ratios (SNR). To solve this problem, we proposed a new superpixel-based spatial weighted sparse nonnegative tensor factorization unmixing model (SupSWNTF), which better exploits the spatial information and improve the sparsity of the solution by adding constraints to the abundance matrix. A series of comparative experimental results on synthetic and real-world data sets show that our algorithm achieves the best unmixing results compared to other state-of-the-art algorithms.
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