  This paper introduces a novel heterogenous domain adaptation (HDA) method for
hyperspectral image classification with a limited amount of labeled samples in
both domains. The method is achieved in the way of cross-domain collaborative
learning (CDCL), which is addressed via cluster canonical correlation analysis
(C-CCA) and random walker (RW) algorithms. To be specific, the proposed CDCL
method is an iterative process of three main stages, i.e. twice of RW-based
pseudolabeling and cross domain learning via C-CCA. Firstly, given the
initially labeled target samples as training set ($\mathbf{TS}$), the RW-based
pseudolabeling is employed to update $\mathbf{TS}$ and extract target clusters
($\mathbf{TCs}$) by fusing the segmentation results obtained by RW and extended
RW (ERW) classifiers. Secondly, cross domain learning via C-CCA is applied
using labeled source samples and $\mathbf{TCs}$. The unlabeled target samples
are then classified with the estimated probability maps using the model trained
in the projected correlation subspace. Thirdly, both $\mathbf{TS}$ and
estimated probability maps are used for updating $\mathbf{TS}$ again via
RW-based pseudolabeling. When the iterative process finishes, the result
obtained by the ERW classifier using the final $\mathbf{TS}$ and estimated
probability maps is regarded as the final classification map. Experimental
results on four real HSIs demonstrate that the proposed method can achieve
better performance compared with the state-of-the-art HDA and ERW methods.

Image and Video Processing;Computer Vision and Pattern Recognition