3 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks
Judy X Yang, Jing Wang, Chen Hong Sui +2
The integration of hyperspectral imaging (HSI) and LiDAR data within new linear feature spaces offers a promising solution to the challenges posed by the high-dimensionality and re…
Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning
Judy X Yang, Jing Wang, Zekun Long +2
Classifying hyperspectral images (HSIs) is a complex task in remote sensing due to the high-dimensional nature and volume of data involved. To address these challenges, we propose…
LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification
Judy X Yang, Jun Zhou, Jing Wang +2
The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectra…
Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder
Judy X Yang, Jun Zhou, Jing Wang +2
Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated o…
HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification
Judy X Yang, Jun Zhou, Jing Wang +2
Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framewor…