# Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating

arXiv: https://arxiv.org/abs/2506.08324 · HTML: https://mlsanity.com/paper/2506.08324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://mlsanity.com/paper/2405.08790/) (2024)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks](https://mlsanity.com/paper/2202.06458/) (2022)
- [DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2307.06667/) (2023)
- [Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group Convolution](https://mlsanity.com/paper/2307.07720/) (2023)
- [Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification](https://mlsanity.com/paper/2502.17879/) (2025)
- [Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification](https://mlsanity.com/paper/2503.23472/) (2025)
- [Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.04463/) (2025)
- [3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.10795/) (2025)
- [Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.13045/) (2025)

## Built on by (most cited first, 1 of 1)

- [MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture](https://mlsanity.com/paper/2507.04409/) (2025)

## Abstract (arXiv)

Deep neural networks face several challenges in hyperspectral image classification, including high-dimensional data, sparse distribution of ground objects, and spectral redundancy, which often lead to classification overfitting and limited generalization capability. To more effectively extract and fuse spatial context with fine spectral information in hyperspectral image (HSI) classification, this paper proposes a novel network architecture called STNet. The core advantage of STNet stems from the dual innovative design of its Spatial-Spectral Transformer module: first, the fundamental explicit decoupling of spatial and spectral attention ensures targeted capture of key information in HSI; second, two functionally distinct gating mechanisms perform intelligent regulation at both the fusion level of attention flows (adaptive attention fusion gating) and the internal level of feature transformation (GFFN). This characteristic demonstrates superior feature extraction and fusion capabilities compared to traditional convolutional neural networks, while reducing overfitting risks in small-sample and high-noise scenarios. STNet enhances model representation capability without increasing network depth or width. The proposed method demonstrates superior performance on IN, UP, and KSC datasets, outperforming mainstream hyperspectral image classification approaches.