{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nonlinear-hyperspectral-unmixing-with-robust","title":"Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization","arxiv_id":"1401.5649","date":"2014-01-22","proceeding":null,"authors":["Cédric Févotte","Nicolas Dobigeon"],"abstract":"This paper introduces a robust mixing model to describe hyperspectral data\nresulting from the mixture of several pure spectral signatures. This new model\nnot only generalizes the commonly used linear mixing model, but also allows for\npossible nonlinear effects to be easily handled, relying on mild assumptions\nregarding these nonlinearities. The standard nonnegativity and sum-to-one\nconstraints inherent to spectral unmixing are coupled with a group-sparse\nconstraint imposed on the nonlinearity component. This results in a new form of\nrobust nonnegative matrix factorization. The data fidelity term is expressed as\na beta-divergence, a continuous family of dissimilarity measures that takes the\nsquared Euclidean distance and the generalized Kullback-Leibler divergence as\nspecial cases. The penalized objective is minimized with a block-coordinate\ndescent that involves majorization-minimization updates. Simulation results\nobtained on synthetic and real data show that the proposed strategy competes\nwith state-of-the-art linear and nonlinear unmixing methods.","url_abs":"http://arxiv.org/abs/1401.5649v2","url_pdf":"http://arxiv.org/pdf/1401.5649v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nonlinear-hyperspectral-unmixing-with-robust","repo_url":"https://github.com/neel-dey/robust-nmf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"hyperspectral-unmixing","task_name":"Hyperspectral Unmixing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}