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Deep Learning Methods Used in Remote Sensing Images: A Review DOI:  https://doi.org/10.30564/jees.v5i1.5232 Received: 3 November 2022 | Revised: 9 February 2023 | Accepted: 20 February 2023 | Published Online: 4 April 2023 Abstract Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a ...

Deep Learning Methods Used in Remote Sensing Images: A Review DOI:  https://doi.org/10.30564/jees.v5i1.5232 Received: 3 November 2022 | Revised: 9 February 2023 | Accepted: 20 February 2023 | Published Online: 4 April 2023 Abstract Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a ...

Deep Learning Methods Used in Remote Sensing Images: A Review DOI:  https://doi.org/10.30564/jees.v5i1.5232 Received: 3 November 2022 | Revised: 9 February 2023 | Accepted: 20 February 2023 | Published Online: 4 April 2023 Abstract Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a ...

Deep Learning Methods Used in Remote Sensing Images: A Review DOI:  https://doi.org/10.30564/jees.v5i1.5232 Received: 3 November 2022 | Revised: 9 February 2023 | Accepted: 20 February 2023 | Published Online: 4 April 2023 Abstract Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a ...