Article · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · January 1, 2025

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

Muhammad Imran Ahmad, Francesco Mauro, Rana Aamir Raza, Manuel Mazzara, Salvatore Distefano, Adil Khan

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.

Remote-Sensing Image ClassificationDomain Adaptation and Few-Shot LearningAdvanced Image Fusion Techniqueshyperspectral image classificationdomain shiftsample diversity

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

Abstract

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