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41
Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis
Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis
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Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis

Remote sensing (Basel, Switzerland), 2023-12, Vol.15 (23), p.5520 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 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/rs15235520

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42
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
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Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Remote sensing (Basel, Switzerland), 2019-07, Vol.11 (14), p.1713 [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/rs11141713

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43
Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape
Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape
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Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Remote sensing (Basel, Switzerland), 2022-09, Vol.14 (18), p.4452-4452 [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/rs14184452

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44
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 [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/rs14030574

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45
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications
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Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications

Remote sensing (Basel, Switzerland), 2023-06, Vol.15 (12), p.3090 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 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/rs15123090

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46
A New Multiple Phenological Spectral Feature for Mapping Winter Wheat
A New Multiple Phenological Spectral Feature for Mapping Winter Wheat
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A New Multiple Phenological Spectral Feature for Mapping Winter Wheat

Remote sensing (Basel, Switzerland), 2022-09, Vol.14 (18), p.4529 [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/rs14184529

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47
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
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Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

Remote sensing (Basel, Switzerland), 2014-02, Vol.6 (2), p.964-983 [Peer Reviewed Journal]

Copyright MDPI AG 2014 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs6020964

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48
Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory
Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory
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Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

Remote sensing (Basel, Switzerland), 2018-01, Vol.10 (4), p.580-580 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10040580 ;PMID: 30147945

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49
Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China
Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China
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Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Remote sensing (Basel, Switzerland), 2016-11, Vol.8 (11), p.954-954 [Peer Reviewed Journal]

Copyright MDPI AG 2016 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8110954

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50
Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data
Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data
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Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

Remote sensing (Basel, Switzerland), 2023-05, Vol.15 (10), p.2599 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 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/rs15102599

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51
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 [Peer Reviewed Journal]

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

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52
Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification
Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification
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Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.185 [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/rs11020185

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53
Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring
Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring
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Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (21), p.4466 [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. ;info:eu-repo/semantics/openAccess ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13214466

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54
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
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A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2019-10, Vol.11 (19), p.2326 [Peer Reviewed Journal]

2019 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/rs11192326

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55
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
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Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2020-04, Vol.12 (7), p.1070 [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/rs12071070

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56
Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data
Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data
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Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (12), p.1470 [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/rs11121470

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57
‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub
‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub
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‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub

Remote sensing (Basel, Switzerland), 2021-08, Vol.13 (16), p.3287 [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/rs13163287

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58
Few-Shot Classification of Aerial Scene Images via Meta-Learning
Few-Shot Classification of Aerial Scene Images via Meta-Learning
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Few-Shot Classification of Aerial Scene Images via Meta-Learning

Remote sensing (Basel, Switzerland), 2021-01, Vol.13 (1), p.108 [Peer Reviewed Journal]

2021. 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/rs13010108

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59
An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data
An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data
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An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data

Remote sensing (Basel, Switzerland), 2020-12, Vol.12 (24), p.4060 [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/rs12244060

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60
Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers
Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers
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Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

Remote sensing (Basel, Switzerland), 2016, Vol.8 (6), p.445-445 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8060445

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