Results 71 to 80 of about 49,483 (287)

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

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

Remote Sensing, 2020
Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to
Xiaofei Yang, Xiaofeng Zhang, Yunming Ye, Raymond Y. K. Lau, Shijian Lu, Xutao Li, Xiaohui Huang   +6 more
doaj   +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

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

European Journal of Remote Sensing, 2020
In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success.
Xibing Zuo, Xuchu Yu, Bing Liu, Pengqiang Zhang, Xiong Tan, Xiangpo Wei   +5 more
doaj   +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

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

Remote Sensing, 2019
This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification.
Majid Shadman Roodposhti, Arko Lucieer, Asim Anees, Brett A. Bryan   +3 more
doaj   +1 more source