Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Geun-Ho Kwak"'
Near-Surface Dispersion and Current Observations Using Dye, Drifters, and HF Radar in Coastal Waters
Autor:
Keunyong Kim, Hong Thi My Tran, Kyu-Min Song, Young Baek Son, Young-Gyu Park, Joo-Hyung Ryu, Geun-Ho Kwak, Jun Myoung Choi
Publikováno v:
Remote Sensing, Vol 16, Iss 11, p 1985 (2024)
This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in
Externí odkaz:
https://doaj.org/article/2cbfeae86aaf4003948f1542e7125dec
Autor:
Geun-Ho Kwak, No-Wook Park
Publikováno v:
Remote Sensing, Vol 16, Iss 7, p 1199 (2024)
The incomplete construction of optical image time series caused by cloud contamination is one of the major limitations facing the application of optical satellite images in crop monitoring. Thus, the construction of a complete optical image time seri
Externí odkaz:
https://doaj.org/article/d22153b2a24f4f43bae8b6d2e4370d20
Publikováno v:
Applied Sciences, Vol 13, Iss 18, p 10233 (2023)
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to in
Externí odkaz:
https://doaj.org/article/bc1c7cb9001d46c9844c76c44809c2b4
Publikováno v:
Applied Sciences, Vol 13, Iss 3, p 1766 (2023)
This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imager
Externí odkaz:
https://doaj.org/article/454449dffd5f4ebbb4f27b3025391bb4
Autor:
Geun-Ho Kwak, No-Wook Park
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4639 (2022)
Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual
Externí odkaz:
https://doaj.org/article/3944793d798f4ffbbf22a94377acf9cd
Publikováno v:
Remote Sensing, Vol 13, Iss 9, p 1629 (2021)
When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with simil
Externí odkaz:
https://doaj.org/article/f5b9be2018634bb59eefaa8b4323a3cf
Autor:
Geun-Ho Kwak, No-Wook Park
Publikováno v:
Applied Sciences, Vol 9, Iss 4, p 643 (2019)
Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spat
Externí odkaz:
https://doaj.org/article/6ad07a777d004f0a9c1f62d525b09df3
Autor:
No-Wook Park, Geun-Ho Kwak
Publikováno v:
Remote Sensing; Volume 14; Issue 18; Pages: 4639
Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7383ea1f6b20be7db0870a555deb496d
Publikováno v:
Remote Sensing, Vol 13, Iss 1629, p 1629 (2021)
Remote Sensing; Volume 13; Issue 9; Pages: 1629
Remote Sensing; Volume 13; Issue 9; Pages: 1629
When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with simil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a074af483739b8d04c8003d7eb31f66
https://www.mdpi.com/2072-4292/13/9/1629
https://www.mdpi.com/2072-4292/13/9/1629
Publikováno v:
Journal of Korean Society on Water Environment. 30:340-350
Recently, TMDL has been implemented to estimate the amount of pollutant loads and to establish proper mitigation strategy to decrease the pollutant loads by the Ministry of Environment. To estimate the amount of pollutant loads with reasonable accura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3554f6905a91e69877f58d2016776bb6
https://doi.org/10.15681/kswe.2014.30.3.340
https://doi.org/10.15681/kswe.2014.30.3.340