In this paper, we tackle the question of discovering an effective set of
spatial filters to solve hyperspectral classification problems. Instead of
fixing a priori the filters and their parameters using expert knowledge, we let
the model find them within random draws in the (possibly infinite) space of
possible filters. We define an active set feature learner that includes in the
model only features that improve the classifier. To this end, we consider a
fast and linear classifier, multiclass logistic classification, and show that
with a good representation (the filters discovered), such a simple classifier
can reach at least state of the art performances. We apply the proposed active
set learner in four hyperspectral image classification problems, including
agricultural and urban classification at different resolutions, as well as
multimodal data. We also propose a hierarchical setting, which allows to
generate more complex banks of features that can better describe the
nonlinearities present in the data.

本文针对通过空间滤波器解决高光谱分类问题，提出了一种使用数据驱动方法找到滤波器的有效集合的方法，并在四个高光谱图像分类问题上实现了良好的分类性能。