Linear spectral unmixing via matrix factorization: Identifiability criteria for sparse abundances

Chia Hsiang Lin, José M. Bioucas Dias

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

In hyperspectral unmixing and in many other areas (e.g., chemometrics, topic modeling, archetypal analysis) simplex-structured matrix factorization (SSMF) plays an essential role as suggested by years of research efforts devoted to this theme. Specifically, SSMF factorizes a data matrix into two matrix factors with one factor (i.e., the abundances) constrained to have its columns lying in the unit simplex. SSMF criteria include the well-known Craig's seminal minimum-volume enclosing simplex (MVES), originally proposed for blind hyperspectral unmixing, and the recently introduced maximum-volume inscribed ellipsoid (MVIE). The identifiability analysis of those criteria is essential to understand their fundamental behavior and also to devise effective SSMF algorithms tailored to the specificities of the different application scenarios. Our analysis is motivated by a simple fact taking place in most remotely sensed hyperspectral mixtures: in most pixels, only a subset of the materials is present. This is to say that the abundances exhibit a form of sparsity and thus lie in the boundary of the data simplex. We then derive some elegant sufficient condition, showing that as long as data points are locally well spread, perfect SSMF identifiability of both criteria can be guaranteed.

原文English
主出版物標題2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面6155-6158
頁數4
ISBN(電子)9781538671504
DOIs
出版狀態Published - 2018 10月 31
事件38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
持續時間: 2018 7月 222018 7月 27

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)
2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
國家/地區Spain
城市Valencia
期間18-07-2218-07-27

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 地球與行星科學(全部)

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