  This paper presents a tensor alignment (TA) based domain adaptation method
for hyperspectral image (HSI) classification. To be specific, HSIs in both
domains are first segmented into superpixels and tensors of both domains are
constructed to include neighboring samples from single superpixel. Then we
consider the subspace invariance between two domains as projection matrices and
original tensors are projected as core tensors with lower dimensions into the
invariant tensor subspace by applying Tucker decomposition. To preserve
geometric information in original tensors, we employ a manifold regularization
term for core tensors into the decomposition progress. The projection matrices
and core tensors are solved in an alternating optimization manner and the
convergence of TA algorithm is analyzed. In addition, a post-processing
strategy is defined via pure samples extraction for each superpixel to further
improve classification performance. Experimental results on four real HSIs
demonstrate that the proposed method can achieve better performance compared
with the state-of-the-art subspace learning methods when a limited amount of
source labeled samples are available.
