RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[3]  Patrick Hostert,et al.  Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping , 2019, Remote Sensing of Environment.

[4]  Dorian Rohner,et al.  Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea , 2018, PloS one.

[5]  Gustavo E. A. P. A. Batista,et al.  Class imbalance revisited: a new experimental setup to assess the performance of treatment methods , 2014, Knowledge and Information Systems.

[6]  Na Zhao,et al.  Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening , 2017, Remote. Sens..

[7]  Thomas Blaschke,et al.  Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches , 2019, Fire.

[8]  Guillaume Cornu,et al.  Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest-Agriculture Mosaics in Temperate and Tropical Landscapes , 2019, Remote. Sens..

[9]  Budiman Minasny,et al.  Addressing the issue of digital mapping of soil classes with imbalanced class observations , 2019, Geoderma.

[10]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[11]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[12]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[13]  Mahdi Hasanlou,et al.  Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.

[14]  Omid Ghorbanzadeh,et al.  National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making , 2020, ISPRS Int. J. Geo Inf..

[15]  ShangJennifer,et al.  Learning from class-imbalanced data , 2017 .

[16]  Mohsen Azadbakht,et al.  Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform LiDAR data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Yang Chen,et al.  Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods , 2019, Remote Sensing of Environment.

[18]  Iñaki Inza,et al.  Measuring the class-imbalance extent of multi-class problems , 2017, Pattern Recognit. Lett..

[19]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[20]  B. Mihai,et al.  Land cover classification in Romanian Carpathians and Subcarpathians using multi-date Sentinel-2 remote sensing imagery , 2017 .

[21]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.

[23]  Hannes Taubenböck,et al.  Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Thomas Blaschke,et al.  An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain , 2017, Arabian Journal of Geosciences.

[25]  Russell G. Congalton,et al.  Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine , 2017, Remote. Sens..

[26]  Janet Franklin,et al.  Mapping land-cover modifications over large areas: A comparison of machine learning algorithms , 2008 .

[27]  Brian Brisco,et al.  Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada , 2017 .

[28]  Fernando Bação,et al.  Imbalanced Learning in Land Cover Classification: Improving Minority Classes' Prediction Accuracy Using the Geometric SMOTE Algorithm , 2019, Remote. Sens..

[29]  Wei Feng,et al.  Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Based on SMOTE and Rotation Forest With Differentiated Sampling Rates , 2019, IEEE Geoscience and Remote Sensing Letters.

[30]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[31]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[32]  Samia Boukir,et al.  Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin , 2015 .

[33]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[34]  Hankui K. Zhang,et al.  Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification , 2017 .

[35]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[36]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[37]  Yijing Li,et al.  Learning from class-imbalanced data: Review of methods and applications , 2017, Expert Syst. Appl..

[38]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[39]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[40]  Weimin Huang,et al.  Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results , 2019, Remote. Sens..

[41]  Edzer Pebesma,et al.  Using Google Earth Engine to detect land cover change: Singapore as a use case , 2018 .

[42]  Ronald E. McRoberts,et al.  Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods - A Case Study from Dak Nong, Vietnam , 2020, Remote. Sens..

[43]  Bruno Tisseyre,et al.  Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale , 2019, OENO One.

[44]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[45]  Mingquan Wu,et al.  Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Sergii Skakun,et al.  Multispectral Misregistration of Sentinel-2A Images: Analysis and Implications for Potential Applications , 2017, IEEE Geoscience and Remote Sensing Letters.

[47]  Mohsen Azadbakht,et al.  Improved Urban Scene Classification Using Full-Waveform Lidar , 2016 .

[48]  Russell G. Congalton,et al.  Global Land Cover Mapping: A Review and Uncertainty Analysis , 2014, Remote. Sens..

[49]  Liu Xiao,et al.  Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data , 2016 .

[50]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[51]  Julio López,et al.  An alternative SMOTE oversampling strategy for high-dimensional datasets , 2019, Appl. Soft Comput..

[52]  Wataru Takeuchi,et al.  Using multiscale texture information from ALOS PALSAR to map tropical forest , 2012 .