  Hyperspectral imaging technology has a wide range of applications, including
forest management, mineral resource exploration, and Earth surface monitoring.
Endmember extraction of hyperspectral images is a key step in leveraging this
technology for applications. It aims to identifying the spectral signatures of
materials, i.e., the major components in the observed scenes. Theoretically
speaking, Hottopixx methods should be effective on problems involving
extracting endmembers from hyperspectral images. Yet, these methods are
challenging to perform in practice, due to high computational costs. They
require us to solve LP problems, called Hottopixx models, whose size grows
quadratically with the number of pixels in the image. It is thus still unclear
as to whether they are actually effective or not. This study clarifies this
situation. We propose an efficient and effective implementation of Hottopixx.
Our implementation follows the framework of column generation, which is known
as a classical but powerful means of solving large-scale LPs. We show in
experiments that our implementation is applicable to the endmember extraction
from real hyperspectral images and can provide estimations of endmember
signatures with higher accuracy than the existing methods can.
