Medical hyperspectral image classification based weakly supervised single-image global learning network
C Zhang, L Mou, S Shan, H Zhang, Y Qi, D Yu… - … Applications of Artificial …, 2024 - Elsevier
Medical hyperspectral imaging provides new possibilities for non-invasive detection and
characterization of diseases, and the processing of images can be accelerated and …
characterization of diseases, and the processing of images can be accelerated and …
Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification
Hyperspectral image classification is a challenging task due to the high dimensionality and
complex nature of hyperspectral data. In recent years, deep learning techniques have …
complex nature of hyperspectral data. In recent years, deep learning techniques have …
An improved 3D-SwinT-CNN network to evaluate the fermentation degree of black tea
F Zhu, J Wang, Y Zhang, J Shi, M He, Z Zhao - Food Control, 2024 - Elsevier
Fermentation is a key process in forming the flavor quality of black tea. Evaluating the
degree of fermentation during black tea processing is difficult. This paper proposes an …
degree of fermentation during black tea processing is difficult. This paper proposes an …
Mind the Gap: Multi-Level Unsupervised Domain Adaptation for Cross-scene Hyperspectral Image Classification
Recently, cross-scene hyperspectral image classification (HSIC) has attracted increasing
attention, alleviating the dilemma of no labeled samples in the target domain (TD). Although …
attention, alleviating the dilemma of no labeled samples in the target domain (TD). Although …
Hyperspectral image classification based on deep separable residual attention network
C Tu, W Liu, L Zhao, T Yan - Infrared Physics & Technology, 2024 - Elsevier
Hyperspectral image have rich spatial and spectral information, and how to fully extract and
utilize the features of these two dimensions is a research hotspot in hyperspectral …
utilize the features of these two dimensions is a research hotspot in hyperspectral …
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based
processing, excels in capturing intricate spatial relationships within images. Spatial-spectral …
processing, excels in capturing intricate spatial relationships within images. Spatial-spectral …
Detection of explosives in dustbins using deep transfer learning based multiclass classifiers
A Gyasi-Agyei - Applied Intelligence, 2024 - Springer
The concealment of improvised explosive devices in dustbins aimed at destroying people
and property is causing the mass removal of dustbins from public places and vehicular …
and property is causing the mass removal of dustbins from public places and vehicular …
Two‐branch global spatial–spectral fusion transformer network for hyperspectral image classification
E Xie, N Chen, G Zhang, J Peng… - The Photogrammetric …, 2024 - Wiley Online Library
Transformer has achieved outstanding performance in hyperspectral image classification
(HSIC) thanks to its effectiveness in modelling the long‐term dependence relation. However …
(HSIC) thanks to its effectiveness in modelling the long‐term dependence relation. However …