TBSA: Tri-Domain Balanced Spectral–Spatial Attention with Deformable Frequency Filtering for Hyperspectral Image Classification
TL;DR
Tri-Domain Balanced Spectral–Spatial Attention (TBSA), a compact frequency-aware framework for HSIC, projects intermediate features into one-dimensional spectral, two-dimensional spatial, and three-dimensional spectral–spatial discrete cosine transform (DCT) domains, and it introduces a deformable frequency filter to adaptively separate low- and high-frequency components.
Abstract
Hyperspectral image classification (HSIC) requires a classifier to distinguish land-cover categories from densely sampled spectral signatures while preserving the spatial arrangement of local materials. Although convolutional networks, Transformer architectures, and recent state-space models have greatly improved spectral–spatial representation learning, three issues remain insufficiently resolved. First, spectral redundancy and local spatial textures are commonly modeled in the original feature domain, where low- and high-frequency responses are only implicitly separated. Second, fixed or weakly adaptive frequency operations cannot reflect the fact that different land-cover classes rely on different spectral smoothness, boundary, and texture cues. Third, spatial evidence, channel selectivity, and frequency responses are often fused by a uniform rule, which may be suboptimal under limited training samples and class imbalance. To address these issues, this paper proposes Tri-Domain Balanced Spectral–Spatial Attention(TBSA), a compact frequency-aware framework for HSIC. TBSA projects intermediate features into one-dimensional spectral, two-dimensional spatial, and three-dimensional spectral–spatial discrete cosine transform (DCT) domains, and it introduces a deformable frequency filter to adaptively separate low- and high-frequency components. Spatial–frequency and spatial–channel interaction form complementary evidence, while the final aggregation rule controls the balance between input-conditioned flexibility, numerical stability, and parameter cost. Experiments on Indian Pines, Houston 2013, and WHU-Hi-LongKou show competitive mean OA and strong class-wise or balanced-accuracy behavior. A five-run inferential analysis does not establish statistically significant OA superiority over the closest DCTN baseline, and the claims are therefore restricted to the observed mean and class-wise results.