# Dual Selective Fusion Transformer Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2410.03171 · HTML: https://mlsanity.com/paper/2410.03171/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 4 Oct 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Large Selective Kernel Network for Remote Sensing Object Detection](https://mlsanity.com/paper/2303.09030/) (2023)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Three dimensional Deep Learning approach for remote sensing image classification](https://mlsanity.com/paper/1806.05824/) (2018)
- [Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2405.04964/) (2024)
- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Binary Change Guided Hyperspectral Multiclass Change Detection](https://mlsanity.com/paper/2112.04493/) (2021)
- [Hyperspectral Image Classification with Attention Aided CNNs](https://mlsanity.com/paper/2005.11977/) (2020)

## Built on by (most cited first, 7 of 7)

- [MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.20509/) (2025)
- [SpectralTrain: A Universal Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2511.16084/) (2025)
- [T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification](https://mlsanity.com/paper/2604.17360/) (2026)
- [High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification](https://mlsanity.com/paper/2604.26279/) (2026)
- [Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity](https://mlsanity.com/paper/2505.11267/) (2025)
- [ORSIFlow: Saliency-Guided Rectified Flow for Optical Remote Sensing Salient Object Detection](https://mlsanity.com/paper/2603.28584/) (2026)
- [Hyperspectral Image Models: Technical Report](https://mlsanity.com/paper/2609.39871/) (2026)

## Abstract (arXiv)

Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification. However, existing Transformer models face two key challenges when dealing with HSI scenes characterized by diverse land cover types and rich spectral information: (1) A fixed receptive field overlooks the effective contextual scales required by various HSI objects; (2) invalid self-attention features in context fusion affect model performance. To address these limitations, we propose a novel Dual Selective Fusion Transformer Network (DSFormer) for HSI classification. DSFormer achieves joint spatial and spectral contextual modeling by flexibly selecting and fusing features across different receptive fields, effectively reducing unnecessary information interference by focusing on the most relevant spatial-spectral tokens. Specifically, we design a Kernel Selective Fusion Transformer Block (KSFTB) to learn an optimal receptive field by adaptively fusing spatial and spectral features across different scales, enhancing the model's ability to accurately identify diverse HSI objects. Additionally, we introduce a Token Selective Fusion Transformer Block (TSFTB), which strategically selects and combines essential tokens during the spatial-spectral self-attention fusion process to capture the most crucial contexts. Extensive experiments conducted on four benchmark HSI datasets demonstrate that the proposed DSFormer significantly improves land cover classification accuracy, outperforming existing state-of-the-art methods. Specifically, DSFormer achieves overall accuracies of 96.59%, 97.66%, 95.17%, and 94.59% in the Pavia University, Houston, Indian Pines, and Whu-HongHu datasets, respectively, reflecting improvements of 3.19%, 1.14%, 0.91%, and 2.80% over the previous model. The code will be available online at https://github.com/YichuXu/DSFormer.