On Hyperspectral Unmixing

In this article the author reviews Jose Bioucas-Dias' key contributions to hyperspectral unmixing (HU), in memory of him as an influential scholar and for his many beautiful ideas introduced to the hyperspectral community. Our story will start with vertex component analysis (VCA)-one of the mos...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 17 - 20
Main Author Ma, Wing-Kin
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.07.2021
Subjects
Geoscience and remote sensing
Hyperspectral sensors
Hyperspectral unmixing
Inference algorithms
Internet
Noise measurement
Optimization
probabilistic simplex component analysis
pure-pixel search
volume minimization
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Summary:In this article the author reviews Jose Bioucas-Dias' key contributions to hyperspectral unmixing (HU), in memory of him as an influential scholar and for his many beautiful ideas introduced to the hyperspectral community. Our story will start with vertex component analysis (VCA)-one of the most celebrated HU algorithms, with more than 2,000 Google Scholar citations. VCA was pioneering, invented at a time when HU research just began to emerge, and it shows sharp insights on a then less-understood subject. Then we will turn to SISAL, another widely-used algorithm. SISAL is not only a highly successful algorithm, it is also a demonstration of its inventor's ingenuity on applied optimization and on smart formulation for practical noisy cases. Our tour will end with dependent component analysis (DECA), perhaps a less well-known contribution. DECA adopts a statistical inference framework, and the author's latest research indicates that such framework has great potential for further development, e.g., there are hidden connections between SISAL and DECA. The development of DECA shows foresight years ahead, in that regard.
ISSN:2153-7003
DOI:10.1109/IGARSS47720.2021.9553289