English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification