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Hyperspectral remote sensing image unsupervised classification, which assigns each pixel of the image into a certain land-cover class without any training samples, plays an important role in the hyperspectral image processing but still leaves huge challenges due to the complicated and high-dimensional data observation. Although many advanced hyperspectral remote sensing image classification techniques based on supervised and semi-supervised learning had been proposed and confirmed effective in recent years, they require a certain number of high quality training samples to learn a classifier, and thus can’t work in the unsupervised manner. In this work, we propose a hyperspectral image unsupervised classification framework based on robust manifold matrix factorization and its out-of-sample extension. In order to address the high feature dimensionality of the hyperspectral image, we propose a unified …