Ground-based image analysis: A tutorial on machine-learning techniques and applications

DC FieldValueLanguage
dc.contributor.authorDev, Soumyabrata-
dc.contributor.authorWen, Bihan-
dc.contributor.authorLee, Yee Hui-
dc.contributor.authorWinkler, Stefan-
dc.date.accessioned2022-01-06T15:16:36Z-
dc.date.available2022-01-06T15:16:36Z-
dc.date.copyright2016 IEEEen_US
dc.date.issued2016-06-07-
dc.identifier.citationIEEE Geoscience and Remote Sensing Magazineen_US
dc.identifier.issn2473-2397-
dc.identifier.urihttp://hdl.handle.net/10197/12703-
dc.description.abstractGround-based whole-sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole-sky imagers (WSI) can have high spatial and temporal resolution, which is an important prerequisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, and more. Extracting the valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine-learning techniques have become available to aid with the image analysis. This article provides a detailed explanation of recent developments in these techniques and their applications in ground-based imaging, aiming to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine-learning techniques in ground-based image analysis via three primary applications: segmentation, classification, and denoising.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen_US
dc.subjectRemote Sensingen_US
dc.subjectImaging science & photographic technologyen_US
dc.subjectCloud detectionen_US
dc.subjectEnergy minimizationen_US
dc.subjectClassificationen_US
dc.subjectSparseen_US
dc.subjectAlgorithmen_US
dc.titleGround-based image analysis: A tutorial on machine-learning techniques and applicationsen_US
dc.typeJournal Articleen_US
dc.internal.authorcontactothersoumyabrata.dev@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume4en_US
dc.identifier.issue2en_US
dc.identifier.startpage79en_US
dc.identifier.endpage93en_US
dc.identifier.doi10.1109/MGRS.2015.2510448-
dc.neeo.contributorDev|Soumyabrata|aut|-
dc.neeo.contributorWen|Bihan|aut|-
dc.neeo.contributorLee|Yee Hui|aut|-
dc.neeo.contributorWinkler|Stefan|aut|-
dc.date.updated2021-12-08T13:25:11Z-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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