3 citations · 7 across the 8 of their papers we have counts for
7 papers · 1 filter
Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks
Zekun Long, Judy X. Yang, Jing Wang +3
Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and…
2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection
Usman Ali, Ali Zia, Abdul Rehman +5
Industrial anomaly detection (IAD) increasingly benefits from integrating 2D and 3D data, but robust cross-modal fusion remains challenging. We propose a novel unsupervised framewo…
HSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion
Judy X Yang, Jing Wang, Zhuanfeng +3
The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications.…
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…
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…