Results 71 to 80 of about 57,062 (333)

Attention-Embedded Triple-Fusion Branch CNN for Hyperspectral Image Classification [PDF]

, 2023
Erlei Zhang, Jiayi Zhang, Jiaxin Bai, Jiarong Bian, Shaoyi Fang, Tao Zhan, Mingchen Feng   +6 more
openalex   +1 more source

Generative Artificial Intelligence Shaping the Future of Agri‐Food Innovation

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Emerging use cases of generative artificial intelligence in agri‐food innovation. ABSTRACT The recent surge in generative artificial intelligence (AI), typified by models such as GPT, diffusion models, and large vision‐language architectures, has begun to influence the agri‐food sector.
Jun‐Li Xu, Dimitrios Argyropoulos, Tamíris da Costa   +2 more
wiley   +1 more source

High Dimensional Feature for Hyperspectral Image Classification

MATEC Web of Conferences, 2018
Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage.
Wang Cailing, Wang Hongwei, Zhang Yinyong, Wen Jia, Yang Fan   +4 more
doaj   +1 more source

Multiple Feature Learning Based on Edge-Preserving Features for Hyperspectral Image Classification

IEEE Access, 2019
The classification of hyperspectral images is the basis and hotspot in the research of hyperspectral images. In this paper, a classification algorithm of hyperspectral image based on multiple edge-preserving features and multiple feature learning (MFL ...
Wei Tian, Lizhong Xu, Zhe Chen, Aiye Shi
doaj   +1 more source

Hyperspectral Image Classification Based on Unsupervised Regularization [PDF]

, 2023
Jian Ji, Shuiqiao Liu, Fangrong Zhang, Xianfu Liao, Shuzhen Wang, Junru Liao   +5 more
openalex   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Hyperspectral Image Classification with Convolutional Neural Networks [PDF]

Proceedings of the 23rd ACM international conference on Multimedia, 2015
Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem. In this paper, however, we propose a feature learning approach for hyperspectral image classification based on convolutional
Slavkovikj, Viktor, Verstockt, Steven, De Neve, Wesley, Van Hoecke, Sofie, Van de Walle, Rik   +4 more
openaire   +2 more sources

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

One-Class Risk Estimation for One-Class Hyperspectral Image Classification [PDF]

, 2022
Hengwei Zhao, Yanfei Zhong, Xin He, Xinyu Wang   +3 more
openalex   +1 more source

Segmentation-Aware Hyperspectral Image Classification

, 2019
To appear at International Geoscience and Remote Sensing Symposium (IGARSS ...
Demirel, Berkan, Ozdil, Omer, Esin, Yunus Emre, Ozturk, Safak   +3 more
openaire   +2 more sources