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1
Pansharpening by Convolutional Neural Networks
Pansharpening by Convolutional Neural Networks
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Pansharpening by Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2016-07, Vol.8 (7), p.594 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8070594

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2
Review of Image Classification Algorithms Based on Convolutional Neural Networks
Review of Image Classification Algorithms Based on Convolutional Neural Networks
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Review of Image Classification Algorithms Based on Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (22), p.4712 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13224712

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3
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
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A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

Remote sensing (Basel, Switzerland), 2019-04, Vol.11 (7), p.765 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11070765

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4
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
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Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

Remote sensing (Basel, Switzerland), 2020-05, Vol.12 (10), p.1667 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12101667

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5
Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote sensing (Basel, Switzerland), 2020-09, Vol.12 (17), p.2735 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12172735

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6
Applications of Remote Sensing in Precision Agriculture: A Review
Applications of Remote Sensing in Precision Agriculture: A Review
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Applications of Remote Sensing in Precision Agriculture: A Review

Remote sensing (Basel, Switzerland), 2020-10, Vol.12 (19), p.3136 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12193136

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7
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges
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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Remote sensing (Basel, Switzerland), 2020-05, Vol.12 (10), p.1688 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12101688

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8
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery
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Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Remote sensing (Basel, Switzerland), 2018-07, Vol.10 (7), p.1119 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10071119

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9
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
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Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Remote sensing (Basel, Switzerland), 2015-12, Vol.7 (12), p.16398-16421 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs71215841

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10
Remote Sensing Image Scene Classification Using CNN-CapsNet
Remote Sensing Image Scene Classification Using CNN-CapsNet
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Remote Sensing Image Scene Classification Using CNN-CapsNet

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.494 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11050494

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11
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
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Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

Remote sensing (Basel, Switzerland), 2020-08, Vol.12 (15), p.2495 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12152495

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12
A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data
A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data
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A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.517 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11050517

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13
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
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Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.523 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11050523

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14
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring
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A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

Remote sensing (Basel, Switzerland), 2016-01, Vol.8 (1), p.70 [Peer Reviewed Journal]

Wageningen University & Research ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8010070

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15
Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks
Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks
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Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1309 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11111309

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16
A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5
A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5
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A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Remote sensing (Basel, Switzerland), 2021-08, Vol.13 (16), p.3095 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13163095

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17
A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images
A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images
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A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images

Remote sensing (Basel, Switzerland), 2022-04, Vol.14 (7), p.1552 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs14071552

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18
A Survey of Deep Learning-Based Human Activity Recognition in Radar
A Survey of Deep Learning-Based Human Activity Recognition in Radar
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A Survey of Deep Learning-Based Human Activity Recognition in Radar

Remote sensing (Basel, Switzerland), 2019-05, Vol.11 (9), p.1068 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11091068

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19
Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review
Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review
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Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

Remote sensing (Basel, Switzerland), 2020-05, Vol.12 (9), p.1444 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12091444

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20
Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery
Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery
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Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.207 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12020207

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