  In this paper, we aim at tackling a general but interesting cross-modality
feature learning question in remote sensing community --- can a limited amount
of highly-discrimin-ative (e.g., hyperspectral) training data improve the
performance of a classification task using a large amount of
poorly-discriminative (e.g., multispectral) data? Traditional semi-supervised
manifold alignment methods do not perform sufficiently well for such problems,
since the hyperspectral data is very expensive to be largely collected in a
trade-off between time and efficiency, compared to the multispectral data. To
this end, we propose a novel semi-supervised cross-modality learning framework,
called learnable manifold alignment (LeMA). LeMA learns a joint graph structure
directly from the data instead of using a given fixed graph defined by a
Gaussian kernel function. With the learned graph, we can further capture the
data distribution by graph-based label propagation, which enables finding a
more accurate decision boundary. Additionally, an optimization strategy based
on the alternating direction method of multipliers (ADMM) is designed to solve
the proposed model. Extensive experiments on two hyperspectral-multispectral
datasets demonstrate the superiority and effectiveness of the proposed method
in comparison with several state-of-the-art methods.
