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Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model is always important to tackle this problem because of its highly illposed nature. In this paper, we introduce a linear spectral mixture model (LMM) based end-to-end deep neural network named as SNMF-Net for hyperspectral unmixing. SNMF-Net shares an alternating architecture and benefits from both model-based methods anddoi:10.1109/tgrs.2021.3081177 fatcat:njcrtqr5ebg23elgxhzy2czuca