Multi-dimensional deep dense residual networks and multiple kernel learning for hyperspectral image classification
H Lv, Y Li, H Zhang, R Wang - Infrared Physics & Technology, 2024 - Elsevier
To address the issues of inadequate feature expression capacity and poor adaptability of
feature fusion in traditional hyperspectral image classification methods, a new approach to …
feature fusion in traditional hyperspectral image classification methods, a new approach to …
PID-HLfusion: Pluggable progressive illumination driven hyperspectral and LiDAR data fusion considering crossmodal geometric structures
W Yu, L Gao, H Huang, Y Shen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Hyperspectral (HS) and light detection and ranging (LiDAR) observation instruments capture
and measure on-ground object properties from complementary perspectives, garnering …
and measure on-ground object properties from complementary perspectives, garnering …
Time domain correlation entropy image conversion: A new method for fault diagnosis of vehicle-mounted cable terminals
K Liu, L Fan, G Nie, K Wang, B Gao, J Fu, J Mu… - Computers and Electrical …, 2024 - Elsevier
The identification of partial discharge (PD) in cable terminals is crucial for the safe operation
of trains. However, the complexity of the operational environment and the similarity of PD …
of trains. However, the complexity of the operational environment and the similarity of PD …
Contrastive Mutual Learning with Pseudo-Label Smoothing for Hyperspectral Image Classification
L Liu, H Zhang, Y Wang - IEEE Transactions on Instrumentation …, 2024 - ieeexplore.ieee.org
Semi-supervised learning has become an effective paradigm for reducing the reliance on
hyperspectral image (HSI) classification on labeled data. State-of-the-art semi-supervised …
hyperspectral image (HSI) classification on labeled data. State-of-the-art semi-supervised …