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Lọc theo: Nhan đề tạp chí: Remote Sensing xóa Neural Networks xóa
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1
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
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Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images

Remote sensing (Basel, Switzerland), 2022-02, Vol.14 (3), p.574 [Tạp chí có phản biện]

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/rs14030574

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2
An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data
An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data
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An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

Remote sensing (Basel, Switzerland), 2021-07, Vol.13 (13), p.2584 [Tạp chí có phản biện]

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. ;Attribution ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13132584

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3
Domain Adversarial Neural Networks for Large-Scale Land Cover Classification
Domain Adversarial Neural Networks for Large-Scale Land Cover Classification
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Domain Adversarial Neural Networks for Large-Scale Land Cover Classification

Remote sensing (Basel, Switzerland), 2019-05, Vol.11 (10), p.1153 [Tạp chí có phản biện]

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/rs11101153

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4
Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities
Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities
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Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

Remote sensing (Basel, Switzerland), 2020-08, Vol.12 (16), p.2602 [Tạp chí có phản biện]

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/rs12162602

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5
Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field
Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field
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Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field

Remote sensing (Basel, Switzerland), 2019-07, Vol.11 (13), p.1565 [Tạp chí có phản biện]

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/rs11131565

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6
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
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Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

Remote sensing (Basel, Switzerland), 2014-06, Vol.6 (6), p.5795-5814 [Tạp chí có phản biện]

Copyright MDPI AG 2014 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs6065795

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7
Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique
Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique
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Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique

Remote sensing (Basel, Switzerland), 2017-10, Vol.9 (10), p.1055 [Tạp chí có phản biện]

Copyright MDPI AG 2017 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9101055

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8
A Multi-Domain Collaborative Transfer Learning Method with Multi-Scale Repeated Attention Mechanism for Underwater Side-Scan Sonar Image Classification
A Multi-Domain Collaborative Transfer Learning Method with Multi-Scale Repeated Attention Mechanism for Underwater Side-Scan Sonar Image Classification
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A Multi-Domain Collaborative Transfer Learning Method with Multi-Scale Repeated Attention Mechanism for Underwater Side-Scan Sonar Image Classification

Remote sensing (Basel, Switzerland), 2022-01, Vol.14 (2), p.355 [Tạp chí có phản biện]

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/rs14020355

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9
Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques
Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques
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Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques

Remote sensing (Basel, Switzerland), 2019-08, Vol.11 (16), p.1916-17 [Tạp chí có phản biện]

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. ;Attribution ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11161916

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10
Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy
Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy
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Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy

Remote sensing (Basel, Switzerland), 2022-09, Vol.14 (18), p.4540 [Tạp chí có phản biện]

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/rs14184540

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Kết quả 1 - 10 của 102  trong Tất cả tài nguyên

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