Results 91 to 100 of about 57,062 (333)

Local Transformer With Spatial Partition Restore for Hyperspectral Image Classification [PDF]

, 2022
Zhaohui Xue, Qi Xu, Mengxue Zhang
openalex   +1 more source

Cross-Domain CNN for Hyperspectral Image Classification [PDF]

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
IGARSS ...
Lee, Hyungtae, Eum, Sungmin, Kwon, Heesung   +2 more
openaire   +2 more sources

Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning

Deep Underground Science and Engineering, EarlyView.
A lithology identification while drilling method was developed, integrating an automated cuttings sampling system, a smart drilling rig, and an ensemble learning model. Underground trials achieved 97.42% accuracy in real‐time identification of cuttings lithology and composition, enhancing hazard management and supporting unmanned drilling technology in
Kun Li, Ting Ren, Ningping Yao, Jon Roberts, Haitao Song, Zhongbei Li, Xueqiu He, Liang Zhao   +7 more
wiley   +1 more source

Harnessing deep learning to quantify microstructural complexity in confocal images of plant protein gels

Food Biomacromolecules, EarlyView.
Abstract Understanding plant protein gel microstructure is key to designing functional food systems. This study introduces a deep learning framework using a U‐Net model with a ResNet34 encoder to segment and quantify confocal laser scanning microscopy (CLSM) images of plant protein gels.
Zhi Yang
wiley   +1 more source

Hyperspectral Image Classification Using MiniVGGNet

, 2021
Hyperspectral image classification is widely used in the analysis of remote sensing images. Recently, deep learning has been seen as the most effective method for hyperspectral image classification. Especially, Convolutional neural networks (CNN) are getting more and more attention in this field.
FIRAT, Hüseyin, UÇAN, Murat, HANBAY, Davut   +2 more
openaire   +2 more sources

Object identification and characterization with hyperspectral imagery to identify structure and function of Natura 2000 habitats [PDF]

, 2010
Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types.
Delalieux, S., Haest, B., Kooistra, L., Mücher, C.A., Schmidt, A., Spanhove, T., Vanden Borre, J., Vermeulen, M.   +7 more
core   +1 more source

Hyperspectral Imaging Combined With Image Fusion Features and Machine Learning to Discriminate Different Origins, Grades, and Shelf‐Life of Oranges

Food Safety and Health, EarlyView.
This study verified that it is feasible to distinguish oranges of different origins, grades and shelf lives by using hyperspectral technology. It covers spectral, image and graph technologies, as well as machine learning and deep learning models. ABSTRACT This study reports the first application of hyperspectral feature fusion technology combined with ...
Honghui Xiao, Hao Hu, Hanyi Mei, Hao Deng, Yongzhi Zhang, Jing Nie, Hao Wu, Karyne M. Rogers, Yuwei Yuan, Chunlin Li   +9 more
wiley   +1 more source

Hyperspectral Remote Sensing Image Classification With CNN Based on Quantum Genetic-Optimized Sparse Representation

IEEE Access, 2020
Due to the characteristics of the spectrum integration, information redundancy, spectrum mixing phenomenon and nonlinearity of the hyperspectral remote sensing images, it is a major challenging task to classify the hyperspectral remote sensing images ...
Huayue Chen, Fang Miao, Xu Shen
doaj   +1 more source

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification [PDF]

, 2023
S. Srinivasan, K. Rajakumar
openalex   +1 more source

Dependent component analysis for hyperspectral image classification

SPIE Proceedings, 2009
Independent component analysis (ICA) has been widely used for hyperspectral image classification in an unsupervised fashion. It is assumed that classes are statistically mutual independent. In practice, this assumption may not be true. In this paper, we apply dependent component analysis (DCA) to unsupervised classification, which does not require the ...
Du, Qian, Kopriva, Ivica
openaire   +2 more sources