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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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2
Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform
Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform
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Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

Remote sensing (Basel, Switzerland), 2021-04, Vol.13 (7), p.1255 [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 (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/rs13071255

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3
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis
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Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis

Remote sensing (Basel, Switzerland), 2023-07, Vol.15 (14), p.3511 [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/rs15143511

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4
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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5
Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves
Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves
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Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves

Remote sensing (Basel, Switzerland), 2022-12, Vol.14 (24), p.6366 [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/rs14246366

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6
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 [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/rs12162602

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7
Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin
Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin
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Comparison of Different Cropland Classification Methods under Diversified Agroecological Conditions in the Zambezi River Basin

Remote sensing (Basel, Switzerland), 2020-07, Vol.12 (13), p.2096 [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/rs12132096

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8
Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification
Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification
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Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1269 [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. ;Attribution ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11111269

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9
Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery
Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery
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Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2015-02, Vol.7 (2), p.2046-2066 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70202046

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10
Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
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Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

Remote sensing (Basel, Switzerland), 2018-12, Vol.10 (12), p.2047 [Peer Reviewed Journal]

2018. 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/rs10122047

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11
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 [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/rs11131565

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12
Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data
Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data
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Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data

Remote sensing (Basel, Switzerland), 2017-10, Vol.9 (10), p.1041 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9101041

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13
Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes
Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes
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Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

Remote sensing (Basel, Switzerland), 2022-06, Vol.14 (11), p.2621 [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/rs14112621

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14
Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
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Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Remote sensing (Basel, Switzerland), 2021-01, Vol.13 (2), p.243 [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/rs13020243

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15
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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16
Comparison of Simulated Multispectral Reflectance among Four Sensors in Land Cover Classification
Comparison of Simulated Multispectral Reflectance among Four Sensors in Land Cover Classification
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Comparison of Simulated Multispectral Reflectance among Four Sensors in Land Cover Classification

Remote sensing (Basel, Switzerland), 2023-04, Vol.15 (9), p.2373 [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/rs15092373

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17
Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China
Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China
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Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China

Remote sensing (Basel, Switzerland), 2023-08, Vol.15 (16), p.3958 [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/rs15163958

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18
A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier
A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier
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A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

Remote sensing (Basel, Switzerland), 2024-01, Vol.16 (3), p.510 [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/rs16030510

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19
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 [Peer Reviewed Journal]

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

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20
DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin
DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin
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DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin

Remote sensing (Basel, Switzerland), 2022-10, Vol.14 (19), p.5000 [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/rs14195000

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