  With a large amount of open satellite multispectral imagery (e.g., Sentinel-2
and Landsat-8), considerable attention has been paid to global multispectral
land cover classification. However, its limited spectral information hinders
further improving the classification performance. Hyperspectral imaging enables
discrimination between spectrally similar classes but its swath width from
space is narrow compared to multispectral ones. To achieve accurate land cover
classification over a large coverage, we propose a cross-modality feature
learning framework, called common subspace learning (CoSpace), by jointly
considering subspace learning and supervised classification. By locally
aligning the manifold structure of the two modalities, CoSpace linearly learns
a shared latent subspace from hyperspectral-multispectral(HS-MS)
correspondences. The multispectral out-of-samples can be then projected into
the subspace, which are expected to take advantages of rich spectral
information of the corresponding hyperspectral data used for learning, and thus
leads to a better classification. Extensive experiments on two simulated HSMS
datasets (University of Houston and Chikusei), where HS-MS data sets have
trade-offs between coverage and spectral resolution, are performed to
demonstrate the superiority and effectiveness of the proposed method in
comparison with previous state-of-the-art methods.
