The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. In this study, we propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for efficient HSI classification. Our approach leverages a pre-trained (initially trained) SST model, fine-tuned iteratively on newly acquired labeled samples using an uncertainty-diversity (Spatial-Spectral Neighborhood Diversity) querying mechanism. This mechanism identifies the most informative and diverse samples, thereby optimizing the transfer learning process to reduce both labeling costs and model uncertainty. We further introduce a dynamic freezing strategy, selectively freezing layers of the SST model to minimize computational overhead while maintaining adaptability to spectral variations in new data. One of the key innovations in our work is the self-calibration of spectral and spatial attention weights, achieved through uncertainty-guided active learning. This not only enhances the model's robustness in handling dynamic and disjoint spectral profiles but also improves generalization across multiple HSI datasets. Additionally, we present a diversity-promoting sampling strategy that ensures the selected samples span distinct spectral regions, preventing overfitting to particular spectral classes. Experiments on benchmark HSI datasets demonstrate that the SST-ATL framework significantly outperforms existing CNN and SST-based methods, offering superior accuracy, efficiency, and computational performance. The source code can be accessed at \url{https://github.com/mahmad000/ATL-SST}.

本研究解决了高光谱图像分类中由于高光谱维度和标签数据有限导致的分类挑战。我们提出了一种新颖的多阶段主动转移学习框架，结合光谱空间变换器和主动学习过程，以提高高光谱图像分类的效率。实验结果表明，该框架在准确性和计算性能上显著优于现有的卷积神经网络和光谱空间变换器方法。

带有主动转移学习的光谱空间变换器用于高光谱图像分类