Results 161 to 170 of about 9,759,453 (201)

Multilabel Sample Augmentation-Based Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2020
The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor ...
Qiaobo Hao, Shutao Li, Xudong Kang
semanticscholar   +2 more sources

Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics

IEEE Journal of Biomedical and Health Informatics, 2022
Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition,
Ruilin Li, Lipo P. Wang, P. Suganthan, O. Sourina   +3 more
semanticscholar   +5 more sources

Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance With Deep Learning Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2023
Labeled samples are important in achieving land cover change detection (LCCD) tasks via deep learning techniques with remote sensing images. However, labeling samples for change detection with bitemporal remote sensing images is labor-intensive and time ...
Z. Lv, Haitao Huang, Weiwei SUN, M. Jia, J. Benediktsson, Fengrui Chen   +5 more
semanticscholar   +1 more source

Novel Land-Cover Classification Approach With Nonparametric Sample Augmentation for Hyperspectral Remote-Sensing Images

IEEE Transactions on Geoscience and Remote Sensing, 2023
Samples play a crucial role in the supervised classification of remote-sensing images. However, labeling large samples for training a classifier or deep-learning network is not only time-consuming, but also labor-intensive.
Z. Lv, Pengfei Zhang, Weiwei SUN, J. Benediktsson, Tao Lei   +4 more
semanticscholar   +1 more source

Comprehensive Sample Augmentation by Fully Considering SSS Imaging Mechanism and Environment for Shipwreck Detection Under Zero Real Samples

IEEE Transactions on Geoscience and Remote Sensing, 2021
To solve the shortage of training samples when using deep learning to detect shipwrecks, a comprehensive sample augmentation method is proposed. The method fully considers the imaging mechanism and environment of side-scan sonar (SSS), such as acoustic ...
Chao Huang, Jianhu Zhao, Yongcan Yu, Hongmei Zhang   +3 more
semanticscholar   +1 more source

RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios

arXiv.org, 2023
The high costs of annotating large datasets suggests a need for effectively training CNNs with limited data, and data augmentation is a promising direction.
Swarna Kamlam Ravindran, Carlo Tomasi
semanticscholar   +1 more source

Sample Augmentation for Intelligent Milling Tool Wear Condition Monitoring Using Numerical Simulation and Generative Adversarial Network

IEEE Transactions on Instrumentation and Measurement, 2021
Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool condition monitoring (TCM) methods.
Qinsong Zhu, Bintao Sun, Yuqing Zhou, Weifang Sun, J. Xiang   +4 more
semanticscholar   +1 more source

Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological ...
Annapurna, Kala, Mounya, Elhilali
openaire   +2 more sources

The Performance Analysis of Time Series Data Augmentation Technology for Small Sample Communication Device Recognition

IEEE Transactions on Reliability, 2023
Communication device recognition is a key problem of electromagnetic space perception. At present, the traditional recognition technology is difficult to adapt to the complex signal situation.
Zhuoran Cai, Wenxuan Ma, Xinrui Wang, Hanhong Wang, Zhongming Feng   +4 more
semanticscholar   +1 more source

Deep Generative Inpainting with Comparative Sample Augmentation

Journal of Computational and Cognitive Engineering, 2022
Recent advances in deep learning techniques such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels.
Boli Fang, Miao Jiang, Jerry Shen, Björn Stenger   +3 more
openaire   +1 more source