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1
Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients
Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients
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Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients

Remote sensing (Basel, Switzerland), 2023-01, Vol.15 (1), p.274 [Peer Reviewed Journal]

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/rs15010274

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2
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
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Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Remote sensing (Basel, Switzerland), 2015-01, Vol.7 (1), p.153-168 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70100153

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3
Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC
Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC
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Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC

Environmental monitoring and assessment, 2023-01, Vol.195 (1), p.3-3, Article 3 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 0167-6369 ;EISSN: 1573-2959 ;DOI: 10.1007/s10661-022-10588-6

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4
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
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Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (21), p.4405 [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/rs13214405

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5
Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning
Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning
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Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Remote sensing (Basel, Switzerland), 2018-02, Vol.10 (2), p.276 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10020276

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6
Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data
Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data
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Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data

Remote sensing (Basel, Switzerland), 2020-07, Vol.12 (13), p.2073 [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/rs12132073

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7
A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances
A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances
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A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

Sustainability, 2018-03, Vol.10 (4), p.1001 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su10041001

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8
A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran
A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran
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A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.105 [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/rs12010105

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9
Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique
Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique
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Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

Sustainability, 2023-01, Vol.15 (1), p.74 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;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: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su15010074

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10
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
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Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1380 [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/rs11111380

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11
Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia
Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia
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Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

Remote sensing (Basel, Switzerland), 2019-04, Vol.11 (7), p.886 [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/rs11070886

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12
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
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Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Remote sensing (Basel, Switzerland), 2019-12, Vol.11 (23), p.2788 [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/rs11232788

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13
Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities
Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities
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Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities

Remote sensing of environment, 2019-03, Vol.223, p.115-129 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0034-4257 ;EISSN: 1879-0704 ;DOI: 10.1016/j.rse.2019.01.018

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14
Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand
Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand
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Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

International journal of environmental research and public health, 2022-06, Vol.19 (13), p.7979 [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. ;2022 by the authors. 2022 ;ISSN: 1660-4601 ;ISSN: 1661-7827 ;EISSN: 1660-4601 ;DOI: 10.3390/ijerph19137979 ;PMID: 35805634

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15
A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems
A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems
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A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

Remote sensing (Basel, Switzerland), 2018-05, Vol.10 (5), p.729 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10050729

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16
Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis
Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis
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Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

Remote sensing (Basel, Switzerland), 2019-02, Vol.11 (3), p.359 [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/rs11030359

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17
Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery
Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery
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Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.245 [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/rs12020245

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18
Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product
Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product
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Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product

Remote sensing (Basel, Switzerland), 2024-04, Vol.16 (7), p.1208 [Peer Reviewed Journal]

2024 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. ;EISSN: 2072-4292 ;DOI: 10.3390/rs16071208

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19
Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification
Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification
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Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification

Remote sensing (Basel, Switzerland), 2024-03, Vol.16 (5), p.895 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 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/rs16050895

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20
Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier
Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier
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Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

Sustainability, 2020-12, Vol.12 (24), p.10324 [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: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su122410324

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