Cross-domain Hyperspectral Image Classification based on Bi-directional Domain Adaptation
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Utilizing hyperspectral remote sensing technology enables the extraction of
fine-grained land cover classes. Typically, satellite or airborne images used
for training and testing are acquired from different regions or times, where
the same class has significant spectral shifts in different scenes. In this
paper, we propose a Bi-directional Domain Adaptation (BiDA) framework for
cross-domain hyperspectral image (HSI) classification, which focuses on
extracting both domain-invariant features and domain-specific information in
the independent adaptive space, thereby enhancing the adaptability and
separability to the target scene. In the proposed BiDA, a triple-branch
transformer architecture (the source branch, target branch, and coupled branch)
with semantic tokenizer is designed as the backbone. Specifically, the source
branch and target branch independently learn the adaptive space of source and
target domains, a Coupled Multi-head Cross-attention (CMCA) mechanism is
developed in coupled branch for feature interaction and inter-domain
correlation mining. Furthermore, a bi-directional distillation loss is designed
to guide adaptive space learning using inter-domain correlation. Finally, we
propose an Adaptive Reinforcement Strategy (ARS) to encourage the model to
focus on specific generalized feature extraction within both source and target
scenes in noise condition. Experimental results on cross-temporal/scene
airborne and satellite datasets demonstrate that the proposed BiDA performs
significantly better than some state-of-the-art domain adaptation approaches.
In the cross-temporal tree species classification task, the proposed BiDA is
more than 3\%$\sim$5\% higher than the most advanced method. The codes will be
available from the website:
https://github.com/YuxiangZhang-BIT/IEEE_TCSVT_BiDA.
