Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2411.18115v2
• Date: Wed, 09 Jul 2025 10:08:45 GMT
• Title: Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Francesco Mauro, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan, Silvia Liberata Ullo,
• Abstract summary: Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data.<n>We propose a novel Active Transfer Learning (ATL) framework built upon a Spatial-Spectral Transformer (SST) backbone.<n>The framework integrates multistage transfer learning with an uncertainty-diversity-driven active learning mechanism.
• Score: 3.087068801861429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address these issues, we propose a novel Active Transfer Learning (ATL) framework built upon a Spatial-Spectral Transformer (SST) backbone. The framework integrates multistage transfer learning with an uncertainty-diversity-driven active learning mechanism that strategically selects highly informative and diverse samples for annotation, thereby significantly reducing labeling costs and mitigating sample redundancy. A dynamic layer freezing strategy is introduced to enhance transferability and computational efficiency, enabling selective adaptation of model layers based on domain shift characteristics. Furthermore, we incorporate a self-calibrated attention mechanism that dynamically refines spatial and spectral weights during adaptation, guided by uncertainty-aware feedback. A diversity-promoting sampling strategy ensures broad spectral coverage among selected samples, preventing overfitting to specific classes. Extensive experiments on benchmark cross-domain HSI datasets demonstrate that the proposed SST-ATL framework achieves superior classification performance compared to conventional approaches. The source code is publicly available at https://github.com/mahmad000/ATL-SST.

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