  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.
