Suchergebnisse - "semisupervised active learning algorithm"

A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification

Autoren: Zengmao Wang Bo Du Lefei Zhang et al.

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 55:3071-3083

Schlagwörter: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ui.adsabs.harvard.edu/abs/2017ITGRS..55.3071W/abstract
https://ieeexplore.ieee.org/abstract/document/7885075
http://ieeexplore.ieee.org/document/7885075/
http://dblp.uni-trier.de/db/journals/tgrs/tgrs55.html#WangDZZJ17
https://dblp.uni-trier.de/db/journals/tgrs/tgrs55.html#WangDZZJ17

A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification.

Autoren: Wang, Zengmao Du, Bo Zhang, Lefei et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Jun2017, Vol. 55 Issue 6, p3071-3083, 13p

Schlagwörter: HYPERSPECTRAL imaging systems, MACHINE learning, IMAGE quality analysis, IMAGE analysis, DATA analysis

Classifying the content of online notepad services using active learning.

Autoren: Al-Nabki, Mhd Wesam Fidalgo, Eduardo Alegre, Enrique et al.

Quelle: Journal of Intelligent Information Systems; Apr2025, Vol. 63 Issue 2, p507-533, 27p

Schlagwörter: CHILD pornography, ARTIFICIAL intelligence, INTERNET content, LAW enforcement agencies, IMAGE processing

A Semi-Supervised Active Learning Neural Network for Data Streams With Concept Drift.

Autoren: Jiao, Botao Gomes, Heitor Murilo Xue, Bing et al.

Quelle: IEEE Computational Intelligence Magazine; Feb2025, Vol. 20 Issue 1, p18-33, 16p

Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples.

Autoren: Li, Yao Zhang, Liyi Chen, Lei et al.

Quelle: Scientific Reports; 1/28/2025, Vol. 15 Issue 1, p1-21, 21p

Schlagwörter: IMAGE recognition (Computer vision), DEEP learning, ARTIFICIAL intelligence, IMAGE processing, IMAGE fusion

Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond.

Autoren: Paheding, Sidike Saleem, Ashraf Siddiqui, Mohammad Faridul Haque et al.

Quelle: Neural Computing & Applications; Sep2024, Vol. 36 Issue 27, p16727-16767, 41p

Schlagwörter: IMAGE recognition (Computer vision), NATURAL language processing, COMPUTER vision, REMOTE sensing, ARTIFICIAL satellites, DEEP learning

Security-aware IoT botnet attack detection framework using dilated and cascaded deep learning mechanism with conditional adversarial autoencoder-based features.

Autoren: Sakthipriya, N. Govindasamy, V. Akila, V.

Quelle: Peer-to-Peer Networking & Applications; May2024, Vol. 17 Issue 3, p1467-1485, 19p

Schlagwörter: BOTNETS, DEEP learning, RECURRENT neural networks, INTERNET of things, COMPUTER network security, DATA scrubbing

TrmGLU-Net: transformer-augmented global-local U-Net for hyperspectral image classification with limited training samples.

Autoren: Liu, Bing Sun, Yifan Wang, Ruirui et al.

Quelle: European Journal of Remote Sensing; Dec2023, Vol. 56 Issue 1, p1-17, 17p

Schlagwörter: IMAGE recognition (Computer vision), SPECTRAL imaging, DEEP learning, CONVOLUTIONAL neural networks, FEATURE extraction

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework.

Autoren: Wang, Yanjun Lin, Yunhao Huang, Huiqing et al.

Quelle: Remote Sensing; Sep2023, Vol. 15 Issue 18, p4432, 24p

Schlagwörter: DEEP learning, SUPERVISED learning, ARTIFICIAL neural networks, REMOTE sensing, IMAGE segmentation, SAMPLING methods

Spectral-Spatial Superpixel Anchor Graph-Based Clustering for Hyperspectral Imagery.

Autoren: Chen, Xiaohong Zhang, Yongshan Feng, Xuxiang et al.

Quelle: IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p

Local–Global Active Learning Based on a Graph Convolutional Network for Semi-Supervised Classification of Hyperspectral Imagery.

Autoren: Ye, Zhen Sun, Tao Shi, Shihao et al.

Quelle: IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p

An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples.

Autoren: Li, Xueying Li, Zongmin Qiu, Huimin et al.

Quelle: Applied Spectroscopy Reviews; 2023, Vol. 58 Issue 6, p367-400, 34p

Schlagwörter: FEATURE extraction, ENVIRONMENTAL monitoring, LAND cover, CLASSIFICATION, PROBLEM solving

Active learning with prediction vector diversity for crop classification in western Inner Mongolia.

Autoren: Su, Tengfei

Quelle: Multimedia Tools & Applications; Apr2023, Vol. 82 Issue 10, p15079-15112, 34p

Schlagwörter: IMAGE recognition (Computer vision), REMOTE sensing, CROPS, SAMPLING (Process), CLASSIFICATION

Geografische Kategorien: INNER Mongolia (China)

Remote Sensing Image Compression Based on the Multiple Prior Information.

Autoren: Fu, Chuan Du, Bo

Quelle: Remote Sensing; Apr2023, Vol. 15 Issue 8, p2211, 16p

Schlagwörter: IMAGE compression, REMOTE sensing, JPEG (Image coding standard)

Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification.

Autoren: Tang, Jiechen Tong, Hengjian Tong, Fei et al.

Quelle: Remote Sensing; Feb2023, Vol. 15 Issue 3, p715, 17p

Schlagwörter: ACTIVE learning, SPATIAL resolution, REMOTE sensing, SUPERVISED learning

Unified active and semi-supervised learning for hyperspectral image classification.

Autoren: Wang, Zengmao Du, Bo

Quelle: GeoInformatica; Jan2023, Vol. 27 Issue 1, p23-38, 16p

Schlagwörter: ACTIVE learning, SUPERVISED learning, REMOTE sensing, LEARNING communities, CLASSIFICATION

An Enhanced Spectral Fusion 3D CNN Model for Hyperspectral Image Classification.

Autoren: Zhou, Junbo Zeng, Shan Xiao, Zuyin et al.

Quelle: Remote Sensing; Nov2022, Vol. 14 Issue 21, p5334, 24p

Schlagwörter: SPECTRAL imaging, MULTISPECTRAL imaging, CONVOLUTIONAL neural networks, DEEP learning

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification.

Autoren: Wang, Nanlan Zeng, Xiaoyong Duan, Yanjun et al.

Quelle: Sensors (14248220); Nov2022, Vol. 22 Issue 21, p8502, 18p

Schlagwörter: HYPERSPECTRAL imaging systems, FEATURE selection, DEEP learning, REMOTE sensing, CLASSIFICATION

Design and Implementation of Trace Inspection System Based upon Hyperspectral Imaging Technology.

Autoren: Wang, Yuchen Ji, Zhongyuan

Quelle: Computational Intelligence & Neuroscience; 7/15/2022, p1-13, 13p

Schlagwörter: MULTISPECTRAL imaging, IMAGE reconstruction, CRIME scene searches, BLOODSTAINS, IMAGE denoising, HYPERSPECTRAL imaging systems, SHOE soles, SPECTRAL imaging

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions.

Autoren: Datta, Debaleena Mallick, Pradeep Kumar Bhoi, Akash Kumar et al.

Quelle: Computational Intelligence & Neuroscience; 4/28/2022, p1-36, 36p

Schlagwörter: DEEP learning, MACHINE learning, SUPPORT vector machines, ACTIVE learning, REMOTE sensing, CLASSIFICATION