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

TL;DR

This paper proposes an unsupervised band selection method that fuses hyperspectral imaging with LiDAR data using attention mechanisms and an Autoencoder, leading to improved classification accuracy.

Contribution

It introduces a novel fusion-based unsupervised band selection framework that integrates HSI and LiDAR data with attention and Autoencoder techniques, which is a new approach in this domain.

Findings

Outperforms existing unsupervised band selection methods

Achieves higher classification accuracy on multiple datasets

Effectively reduces data redundancy through fusion and attention

Abstract

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.