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Article · IEEE Transactions on Geoscience and Remote Sensing · January 1, 2026

Iformer: Irregular Spatial–Spectral Transformer for Hyperspectral Image Classification

Muhammad Imran Ahmad, Manuel Mazzara

Manuel Mazzara
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Abstract

The hyperspectral image (HSI) classification (HSIC) remains challenging due to severe class imbalance dominated by background pixels, thin and edge-adjacent structures with anisotropic context, nonstationary spectral responses across materials, and illumination variability in large HSI cubes. The existing convolutional and transformer-based methods often rely on stationary kernels and uniform spectral sampling, which limits their ability to model irregular spatial–spectral dependencies. This work introduces Iformer, a band-aware irregular spatial–spectral transformer that incorporates coordinate-constrained spectral modeling and geometry-aware spatial propagation. The proposed framework is built upon three core components. First, the irregular spectral aggregation (ISA) models the spectral axis using coordinate-aware anchors with content-gated attention, enabling adaptation to spectral nonstationarity while reducing spectral tokens ($S' \to R_{\mathrm {ref}} \ll S'$) to control computational complexity. Second, irregular spatial propagation ($\mathrm {ISA}_{\mathrm {spa}}$) introduces a coordinate-driven anisotropic kernel to capture directional spatial context, improving discrimination of fine and structurally complex regions. Third, irregular multihead self-attention (iMHSA) enables heterogeneous subspace allocation across attention heads, allowing the model to adapt its representational capacity to varying spectral–spatial complexities. A unified spatial–spectral embedding supports these components via a 3-D patchifier with channel reweighting and a calibrated CLS+GAP readout to improve prediction stability under class imbalance. Extensive experiments on four widely used benchmarks demonstrate that Iformer consistently achieves superior accuracy.

Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsSpectroscopy and Chemometric Analyseshyperspectral image classificationclass imbalancemulti-head self-attention

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