# Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder

arXiv: https://arxiv.org/abs/2404.05258 · HTML: https://mlsanity.com/paper/2404.05258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [Classification of Hyperspectral and LiDAR Data Using Coupled CNNs](https://mlsanity.com/paper/2002.01144/) (2020)
- [Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification](https://mlsanity.com/paper/2103.08741/) (2021)
- [LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification](https://mlsanity.com/paper/2404.03883/) (2024)
- [Optimal Clustering Framework for Hyperspectral Band Selection](https://mlsanity.com/paper/1904.13036/) (2019)
- [Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks](https://mlsanity.com/paper/2106.00645/) (2021)
- [Hyperspectral and LiDAR data classification based on linear self-attention](https://mlsanity.com/paper/2104.02301/) (2021)
- [Unsupervised multi-branch Capsule for Hyperspectral and LiDAR classification](https://mlsanity.com/paper/2206.07348/) (2022)

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

- [Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification](https://mlsanity.com/paper/2607.24160/) (2026)

## Abstract (arXiv)

Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated on analysing spectral and pixel characteristics within individual bands independently. These approaches overlook the potential benefits of integrating multiple data sources, such as Light Detection and Ranging (LiDAR), and is further challenged by the limited availability of labeled data in HSI processing, which represents a significant obstacle. To address these challenges, this paper introduces a novel unsupervised band selection framework that incorporates attention mechanisms and an Autoencoder for reconstruction-based band selection. Our methodology distinctively integrates HSI with LiDAR data through an attention score, using a convolutional Autoencoder to process the combined feature mask. This fusion effectively captures essential spatial and spectral features and reduces redundancy in hyperspectral datasets. A comprehensive comparative analysis of our innovative fused band selection approach is performed against existing unsupervised band selection and fusion models. We used data sets such as Houston 2013, Trento, and MUUFLE for our experiments. The results demonstrate that our method achieves superior classification accuracy and significantly outperforms existing models. This enhancement in HSI band selection, facilitated by the incorporation of LiDAR features, underscores the considerable advantages of integrating features from different sources.