Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Hamid Salehi Shahrabi"'
Publikováno v:
International Journal of Mining and Geo-Engineering, Vol 58, Iss 3, Pp 315-322 (2024)
In recent times, geological remote sensing has greatly enhanced the ability to access alteration zones and identify potential sites for hydrothermal deposits quickly and cost-effectively. This study will utilize satellite image processing methods to
Externí odkaz:
https://doaj.org/article/6454daaa48a344598059d31689fd60c1
Autor:
Kourosh Kabir, Ali Taherinia, Davoud Ashourloo, Ahmad Khosravi, Hossien Karim, Hamid Salehi Shahrabi, Mojtaba Hedayat Yaghoobi, Alireza Soleimani, Zaynab Siami, Mohammad Noorisepehr, Ramin Tajbakhsh, Mohammad Reza Maghsoudi, Mehran Lak, Parham Mardi, Behnaz Nouri, Mohammad Mohammadzadeh, Mehdi Azimzadeh, Mahmood Bakhtiyari
Publikováno v:
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-10 (2021)
Abstract Background The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic r
Externí odkaz:
https://doaj.org/article/f90a32ee2802406e9f30f7e78a7b19e0
Publikováno v:
Journal of Remote Sensing and Gis. 15:1-15
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29dd83715bc7fee381f861baeb06ea89
https://doi.org/10.52547/gisj.15.1.1
https://doi.org/10.52547/gisj.15.1.1
Publikováno v:
Journal of Remote Sensing and Gis. 13:59-72
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8a09f6238335e662c281d0d2bbe9289
https://doi.org/10.52547/gisj.13.3.59
https://doi.org/10.52547/gisj.13.3.59
Autor:
Mohsen Azadbakht, Amir Moeini Rad, Hamed Nematollahi, Davoud Ashourloo, Hamid Salehi Shahrabi, Hossein Aghighi
Publikováno v:
International Journal of Remote Sensing. 41:8406-8427
Recently, availability of valuable high spatial and temporal resolution optical satellite imagery such as Sentinel-2 has provided a great opportunity for a wide variety of crop-related studies incl...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::768e25a507685b102e4d6a26c91b2e8e
https://doi.org/10.1080/01431161.2020.1779377
https://doi.org/10.1080/01431161.2020.1779377
Autor:
Alireza Soleimani, Mahmood Bakhtiyari, Behnaz Nouri, Mehran Lak, Mohammad Reza Maghsoudi, Ali Taherinia, Ahmad Khosravi, Mehdi Azimzadeh, Hamid Salehi Shahrabi, Mohammad Noorisepehr, Ramin Tajbakhsh, Kourosh Kabir, Hossien Karim, Parham Mardi, Zaynab Siami, Davoud Ashourloo, Mojtaba Hedayat Yaghoobi, Mohammad Mohammadzadeh
Publikováno v:
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-10 (2021)
BMC Infectious Diseases
BMC Infectious Diseases
Background The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic reproducti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e024db0cfe8a5d3558b4e8c14b5e838
https://doi.org/10.1186/s12879-021-06870-6
https://doi.org/10.1186/s12879-021-06870-6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12:1471-1481
Phenology-based classification methods have been developed with the goal of removing the need for ground sampling. In contrast, machine-learning-based classification methods are costly since they need extensive ground truth data to be collected, a pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bc6ba2aa2667504695e822089464d50
https://doi.org/10.1109/jstars.2019.2906684
https://doi.org/10.1109/jstars.2019.2906684
Autor:
Hamid Salehi Shahrabi, Hossein Aghighi, Davoud Ashourloo, Saeid Homayouni, Soheil Radiom, Ali Akbar Matkan, Mohsen Azadbakht
Publikováno v:
Remote Sensing Applications: Society and Environment. 25:100657
Remote Sensing (RS) technology provides regular monitoring of alfalfa farms, as a major source of forage production worldwide. Phenological characteristics derived from time series of RS imagery provide a valuable information source to estimate crop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9669f831e2a4c86b2630b4c0faf4db76
https://doi.org/10.1016/j.rsase.2021.100657
https://doi.org/10.1016/j.rsase.2021.100657
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11:4563-4577
Machine learning (ML) techniques have been utilized for the crop monitoring and yield estimation/prediction using remotely sensed data. However, these methods have been investigated less for yield prediction of some crops, such as silage maize, which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98698c765fda6cb452a970decd14cfc1
https://doi.org/10.1109/jstars.2018.2823361
https://doi.org/10.1109/jstars.2018.2823361
Autor:
Soheil Radiom, Hamid Salehi Shahrabi, Mohsen Azadbakht, Hossein Aghighi, Davoud Ashourloo, Ali Akbar Matkan
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11:4478-4487
Remote sensing (RS) data have been utilized increasingly for mapping various crops at local and regional scales using various techniques. However, training data collection of these methods is costly and time consuming. On the other hand, time series
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9f239aef2be9028c343ebf54979f634
https://doi.org/10.1109/jstars.2018.2874726
https://doi.org/10.1109/jstars.2018.2874726