# Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2412.03893 · HTML: https://mlsanity.com/paper/2412.03893/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 78 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.06133/) (2019)
- [Nonlinear unmixing of hyperspectral images: models and algorithms](https://mlsanity.com/paper/1304.1875/) (2013)
- [Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution](https://mlsanity.com/paper/1905.01752/) (2019)
- [A Plug-and-Play Priors Framework for Hyperspectral Unmixing](https://mlsanity.com/paper/2012.13074/) (2020)

## Built on by (most cited first, 4 of 4)

- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.09948/) (2026)
- [AGSA-Net: Abundance-Guided Self-Attention Network for Spectral Unmixing-Aware Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2609.06359/) (2026)
- [Token Clustering and Semantic Sequence Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2609.28580/) (2026)

## Abstract (arXiv)

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex network architecture design, while ignoring the existence of mixed pixels in actual scenarios. To tackle this difficulty, we propose a novel dual-branch subpixel-guided network for HSI classification, called DSNet, which automatically integrates subpixel information and convolutional class features by introducing a deep autoencoder unmixing architecture to enhance classification performance. DSNet is capable of fully considering physically nonlinear properties within subpixels and adaptively generating diagnostic abundances in an unsupervised manner to achieve more reliable decision boundaries for class label distributions. The subpixel fusion module is designed to ensure high-quality information fusion across pixel and subpixel features, further promoting stable joint classification. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of DSNet compared with state-of-the-art DL-based HSI classification approaches. The codes will be available at https://github.com/hanzhu97702/DSNet, contributing to the remote sensing community.