  Data-hungry HSI classification methods require high-quality labeled HSIs,
which are often costly to obtain. This characteristic limits the performance
potential of data-driven methods when dealing with limited annotated samples.
Bridging the domain gap between data acquired from different sensors allows us
to utilize abundant labeled data across sensors to break this bottleneck. In
this paper, we propose a novel Attention-Gated Tuning (AGT) strategy and a
triplet-structured transformer model, Tri-Former, to address this issue. The
AGT strategy serves as a bridge, allowing us to leverage existing labeled HSI
datasets, even RGB datasets to enhance the performance on new HSI datasets with
limited samples. Instead of inserting additional parameters inside the basic
model, we train a lightweight auxiliary branch that takes intermediate features
as input from the basic model and makes predictions. The proposed AGT resolves
conflicts between heterogeneous and even cross-modal data by suppressing the
disturbing information and enhances the useful information through a soft gate.
Additionally, we introduce Tri-Former, a triplet-structured transformer with a
spectral-spatial separation design that enhances parameter utilization and
computational efficiency, enabling easier and flexible fine-tuning. Comparison
experiments conducted on three representative HSI datasets captured by
different sensors demonstrate the proposed Tri-Former achieves better
performance compared to several state-of-the-art methods. Homologous,
heterologous and cross-modal tuning experiments verified the effectiveness of
the proposed AGT. Code has been released at:
\href{https://github.com/Cecilia-xue/AGT}{https://github.com/Cecilia-xue/AGT}.
