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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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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
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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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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
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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
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Land Cover Changes Based on Landsat Imagery Interpretation
Jurnal Teknik Pertanian Lampung, 2023 This paper presents the use of satellite data (i.e., Landsat-5 & Landsat-8) to interpret the change of land cover from 1997 to 2020. The study area covers the administrative boundary of Lumajang Regency.
Chairiyah Umi Rahayu +4 more
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An Improved Algorithm of Drift Compensation for Olfactory Sensors
Applied Sciences, 2022 This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm.
Siyu Lu +6 more
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Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set
Entropy, 2022 To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes.
Yi Kang +3 more
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Supervised Classification Of induction Motors faults [PDF]
E3S Web of Conferences, 2022 Currently, the environmental challenges have been considered as a strategic issue for most industrial companies around the world, threatening their sustainability and profit; This leads to taking the environmental dimensions seriously and preserving ...
El bouisfi Radouane +2 more
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