skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Results 1 - 20 of 208  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page
Refined by: Journal Title: Remote Sensing remove Database: SWEPUB Freely available online remove
Result Number Material Type Add to My Shelf Action Record Details and Options
1
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
Material Type:
Article 		Add to My Research

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2016-04, Vol.8 (4), p.329 [Peer Reviewed Journal]

Copyright MDPI AG 2016 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8040329

Full text available

Citations Cited by
2
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Material Type:
Article 		Add to My Research

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.266 [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/rs12020266

Full text available

Citations Cited by
3
Tree Species Classification with Multi-Temporal Sentinel-2 Data
Tree Species Classification with Multi-Temporal Sentinel-2 Data
Material Type:
Article 		Add to My Research

Tree Species Classification with Multi-Temporal Sentinel-2 Data

Remote sensing (Basel, Switzerland), 2018-11, Vol.10 (11), p.1794 [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/rs10111794

Full text available

Citations Cited by
4
Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data
Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data
Material Type:
Article 		Add to My Research

Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

Remote sensing (Basel, Switzerland), 2017-12, Vol.9 (12), p.1271 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9121271

Full text available

Citations Cited by
5
Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms
Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms
Material Type:
Article 		Add to My Research

Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

Remote sensing (Basel, Switzerland), 2021-04, Vol.13 (7), p.1349 [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/rs13071349

Full text available

Citations Cited by
6
Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine
Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine
Material Type:
Article 		Add to My Research

Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.76 [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/rs12010076

Full text available

Citations Cited by
7
Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine
Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine
Material Type:
Article 		Add to My Research

Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine

Remote sensing (Basel, Switzerland), 2021-06, Vol.13 (13), p.2565 [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/rs13132565

Full text available

Citations Cited by
8
Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters
Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters
Material Type:
Article 		Add to My Research

Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

Remote Sensing, 2016-06, Vol.8 (6), p.497 [Peer Reviewed Journal]

Attribution ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs8060497

Full text available

Citations Cited by
9
Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
Material Type:
Article 		Add to My Research

Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest

Remote sensing (Basel, Switzerland), 2015, Vol.7 (8), p.10017-10041 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70810017

Full text available

Citations Cited by
10
Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera
Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera
Material Type:
Article 		Add to My Research

Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera

Remote sensing (Basel, Switzerland), 2019-03, Vol.11 (5), p.567 [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/rs11050567

Full text available

Citations Cited by
11
Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea
Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea
Material Type:
Article 		Add to My Research

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Remote sensing (Basel, Switzerland), 2017-10, Vol.9 (10), p.1070 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9101070

Full text available

Citations Cited by
12
A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data
A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data
Material Type:
Article 		Add to My Research

A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data

Remote sensing (Basel, Switzerland), 2018-04, Vol.10 (4), p.635 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10040635

Full text available

Citations Cited by
13
NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China
NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China
Material Type:
Article 		Add to My Research

NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China

Remote sensing (Basel, Switzerland), 2020-12, Vol.12 (24), p.4138 [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/rs12244138

Full text available

Citations Cited by
14
MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
Material Type:
Article 		Add to My Research

MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane

Remote sensing (Basel, Switzerland), 2017-10, Vol.9 (10), p.1052 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9101052

Full text available

Citations Cited by
15
Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor
Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor
Material Type:
Article 		Add to My Research

Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Remote sensing (Basel, Switzerland), 2021-02, Vol.13 (4), p.577 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13040577

Full text available

Citations Cited by
16
Ground Validation of GPM IMERG Precipitation Products over Iran
Ground Validation of GPM IMERG Precipitation Products over Iran
Material Type:
Article 		Add to My Research

Ground Validation of GPM IMERG Precipitation Products over Iran

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.48 [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/rs12010048

Full text available

Citations Cited by
17
Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone
Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone
Material Type:
Article 		Add to My Research

Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone

Remote sensing (Basel, Switzerland), 2017-05, Vol.9 (5), p.485 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs9050485

Full text available

Citations Cited by
18
A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
Material Type:
Article 		Add to My Research

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

Remote sensing (Basel, Switzerland), 2022-01, Vol.14 (1), p.211 [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/rs14010211

Full text available

Citations Cited by
19
Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing
Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing
Material Type:
Article 		Add to My Research

Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

Remote sensing (Basel, Switzerland), 2021-04, Vol.13 (8), p.1597 [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/rs13081597

Full text available

Citations Cited by
20
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Material Type:
Article 		Add to My Research

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Remote sensing (Basel, Switzerland), 2020-09, Vol.12 (17), p.2742 [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/rs12172742

Full text available

Citations Cited by

Results 1 - 20 of 208  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait