On Hyperspectral Unmixing

TL;DR

This paper reviews José Bioucas-Dias' influential contributions to hyperspectral unmixing, highlighting key algorithms like VCA, SISAL, and DECA, and discusses their significance, innovations, and potential future directions.

Contribution

It provides a comprehensive overview of Bioucas-Dias' pioneering algorithms and insights in hyperspectral unmixing, emphasizing their impact and underlying ideas.

Findings

VCA is a highly cited pioneering HU algorithm.

SISAL demonstrates effective optimization in noisy conditions.

DECA introduces a statistical inference framework with promising connections to other methods.

Abstract

In this article the author reviews Jos\'e 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.