  In this paper, we study the problem of hyperspectral pixel classification
based on the recently proposed architectures for compressive whisk-broom
hyperspectral imagers without the need to reconstruct the complete data cube. A
clear advantage of classification in the compressed domain is its suitability
for real-time on-site processing of the sensed data. Moreover, it is assumed
that the training process also takes place in the compressed domain, thus,
isolating the classification unit from the recovery unit at the receiver's
side. We show that, perhaps surprisingly, using distinct measurement matrices
for different pixels results in more accuracy of the learned classifier and
consistent classification performance, supporting the role of information
diversity in learning.
