Results 51 to 60 of about 53,858 (186)
A recurrent neural network for classification of unevenly sampled variable stars
, 2017 Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to ...
Bloom, Joshua S. +3 more
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Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images [PDF]
, 1999 One of the most relevant problems in the extraction of scientifically useful information from wide field astronomical images (both photographic plates and CCD frames) is the recognition of the objects against a noisy background and their classification ...
Andreon, S. +4 more
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Unsupervised hyperspectral band selection via multi-feature information-maximization clustering [PDF]
2017 IEEE International Conference on Image Processing (ICIP), 2017 This paper presents a new approach for unsupervised band selection in the context of hyperspectral imaging. The hyperspectral band selection (HBS) task is considered as a clustering problem: bands are clustered in the image space; one representative image is then kept for each cluster, to be part of the set of selected bands.
Marco Bevilacqua, Yannick Berthoumieu
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Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting
IEEE Access, 2019 Iteratively reweighted multivariate alteration detection algorithm has the phenomena of broken patches, much noise, and small change area that are difficult to detect, and the overall detection rate is low.
Liyuan Ma +4 more
doaj +1 more source
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
, 2017 One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's ...
Boucsein W. +3 more
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms [PDF]
, 2005 Methods for improving supervised and unsupervised classification of remotely sensed data were developed in this study. Supervised classification of remotely sensed data requires systematic collection of training samples for classes of interest.
Teoh, Chin Chuang
core
International Journal of Digital EarthThis study investigates the integration of Geospatial Foundation Models (GFMs) into an unsupervised change detection workflow for landslide identification.
Julia Anna Leonardi +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral remote sensing image (HSI) can record complete and continuous spectral curves of land features while obtaining the image information, which can achieve precise classification.
Yuting Wan +3 more
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Sensors, 2020 Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It
Agnieszka Wosiak, Aleksandra Dura
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Multiple-Feature Kernel-Based Probabilistic Clustering for Unsupervised Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 This paper presents a new method to perform unsupervised band selection (UBS) with hyperspectral data. The method provides a probabilistic clustering approach. The band images are clustered in the image space by computing their posterior class probability.
Marco Bevilacqua, Yannick Berthoumieu
openaire +1 more source