Ensemble Alignment Subspace Adaptation Method for Cross-Scene Classification

doi:10.1109/LGRS.2023.3256348

Ensemble Alignment Subspace Adaptation Method for Cross-Scene Classification

2023-01-01

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DOI：10.1109/LGRS.2023.3256348

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An ensemble alignment subspace adaptation (EASA) method is proposed in this letter for the cross-scene classification. It can settle the problem of both foreign objects in the same spectrum and different spectra. The algorithm combines the idea of ensemble learning with the domain adaptive (DA) algorithm. Considering the sample imbalance problem of the original data (OD), the source data (SD) are obtained by multiple random sampling of OD according to certain rules and used as input. Then, geometric alignment and statistical alignment of SD and target data (TD) are performed to build a communal subspace, followed by the classification of TD. The classification labels are finally ensembled by counting the multiple classification results with retaining valid information. This technique can reduce the uncertainty and randomness of generating subspace projections. The experimental results on two real datasets show that the proposed algorithm has a terrific accuracy improvement compared with the traditional machine learning and DA methods.

Keywords:

Ensemble learning

Hyperspectral imaging

Multiprotocol label switching

Geoscience and remote sensing

Bagging

Training

Buildings

Cross-scene classification

domain adaptive (DA)

ensemble learning

hyperspectral

transfer learning

Ensemble Alignment Subspace Adaptation Method for Cross-Scene Classification

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