Superpixel-based spatial weighted sparse nonnegative tensor factorization unmixing algorithm

Ningyuan Zhang; Chengzhi Deng; Shaoquan Zhang; Fan Li; Pengfei Lai; Min Huang; Shengqian Wang

doi:10.1117/12.2665583

31 January 2023 Superpixel-based spatial weighted sparse nonnegative tensor factorization unmixing algorithm

Ningyuan Zhang, Chengzhi Deng, Shaoquan Zhang, Fan Li, Pengfei Lai, Min Huang, Shengqian Wang

Proceedings Volume 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022); 1250522 (2023) https://doi.org/10.1117/12.2665583
Event: Earth and Space: From Infrared to Terahertz (ESIT 2022), 2022, Nantong, China

Abstract

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.

Citation Download Citation

Ningyuan Zhang, Chengzhi Deng, Shaoquan Zhang, Fan Li, Pengfei Lai, Min Huang, and Shengqian Wang "Superpixel-based spatial weighted sparse nonnegative tensor factorization unmixing algorithm", Proc. SPIE 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022), 1250522 (31 January 2023); https://doi.org/10.1117/12.2665583

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KEYWORDS

Image segmentation

Hyperspectral imaging

Signal to noise ratio

Image processing algorithms and systems

Data modeling

Image enhancement

Image sensors

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