doi:10.1109\/ICASSP40776.2020.9053239<\/a><\/p>\n\n\n\nAbstract. <\/strong>Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective non-negative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing; However, it suffers several issues that prohibit its practical application. In this work, we propose an unsupervised nonlinear unmixing method that overcomes these weaknesses. Specifically, the new method introduces into each pixel a parameter that adjusts the nonlinearity therein. These parameters are jointly optimized with endmembers and abundances, using a carefully designed objective function by multiplicative update rules. Experiments on synthetic and real datasets confirm the effectiveness of the proposed method.<\/p>\n","protected":false},"excerpt":{"rendered":"By Fei Zhu, Paul Honeine, Jie Chen. In Proc. 45th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4 \u2013 8 May 2020. \u00a0link\u00a0\u00a0\u00a0\u00a0paper\u00a0\u00a0\u00a0doi:10.1109\/ICASSP40776.2020.9053239 Abstract. Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective non-negative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing; … Continue reading Pixel-wise linear\/nonlinear nonnegative matrix factorization for unmixing of hyperspectral data (in ICASSP’20)<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[11,12,13,14,15,16],"class_list":["post-43","post","type-post","status-publish","format-standard","hentry","category-conferences","tag-bi-objective-optimization","tag-hyperspectral-data-analysis","tag-kernel-methods","tag-nonlinear-unmixing","tag-nonnegative-matrix-factorization","tag-pre-image-problem"],"_links":{"self":[{"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/posts\/43","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/comments?post=43"}],"version-history":[{"count":3,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/posts\/43\/revisions"}],"predecessor-version":[{"id":46,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/posts\/43\/revisions\/46"}],"wp:attachment":[{"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/media?parent=43"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/categories?post=43"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/projets.litislab.fr\/api\/wp-json\/wp\/v2\/tags?post=43"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"Pixel-wise](\"http:\/\/bibbase.org\/img\/filetypes\/pdf.svg\" "\\"")
\u00a0paper<\/span><\/a>\u00a0\u00a0\u00a0doi:10.1109\/ICASSP40776.2020.9053239<\/a><\/p>\n\n\n\n