Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification
Remote Sensing, 2022 Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this
Liangliang Song +4 more
doaj +3 more sources
A review on graph-based semi-supervised learning methods for hyperspectral image classification
Egyptian Journal of Remote Sensing and Space Sciences, 2020 In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
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Variational Information Bottleneck for Semi-Supervised Classification [PDF]
Entropy, 2020 In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. We apply a variational decomposition of mutual information terms of IB.
Slava Voloshynovskiy +4 more
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Supervised classification enables rapid annotation of cell atlases. [PDF]
Nat Methods, 2019 Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting.
Pliner HA, Shendure J, Trapnell C.
europepmc +2 more sources
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. [PDF]
Nat Commun, 2019 Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep
Fries JA +13 more
europepmc +2 more sources
Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
doaj +1 more source
DO WE STILL NEED IMAGENET PRE-TRAINING IN REMOTE SENSING SCENE CLASSIFICATION? [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a de facto standard in remote sensing scene classification. Recently, the availability of larger high resolution remote sensing (HRRS) image datasets and progress in ...
V. Risojević, V. Stojnić
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SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification [PDF]
Computer Vision and Pattern Recognition, 2021 A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels.
Zijian Hu +3 more
semanticscholar +1 more source
Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification [PDF]
International Joint Conference on Artificial Intelligence, 2020 Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.
Yunsheng Shi +5 more
semanticscholar +1 more source
Remote Sensing, 2022 The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas.
Lei Zhao +4 more
doaj +1 more source