  Insufficient prior knowledge of a captured hyperspectral image (HSI) scene
may lead the experts or the automatic labeling systems to offer incorrect
labels or ambiguous labels (i.e., assigning each training sample to a group of
candidate labels, among which only one of them is valid; this is also known as
partial label learning) during the labeling process. Accordingly, how to learn
from such data with ambiguous labels is a problem of great practical
importance. In this paper, we propose a novel superpixelwise low-rank
approximation (LRA)-based partial label learning method, namely SLAP, which is
the first to take into account partial label learning in HSI classification.
SLAP is mainly composed of two phases: disambiguating the training labels and
acquiring the predictive model. Specifically, in the first phase, we propose a
superpixelwise LRA-based model, preparing the affinity graph for the subsequent
label propagation process while extracting the discriminative representation to
enhance the following classification task of the second phase. Then to
disambiguate the training labels, label propagation propagates the labeling
information via the affinity graph of training pixels. In the second phase, we
take advantage of the resulting disambiguated training labels and the
discriminative representations to enhance the classification performance. The
extensive experiments validate the advantage of the proposed SLAP method over
state-of-the-art methods.
