RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine
暂无分享,去创建一个
Mohsen Azadbakht | Jinhu Bian | Meisam Amani | Hamid Ebrahimy | Amin Naboureh | M. Amani | Amin Naboureh | M. Azadbakht | Jinhu Bian | Hamid Ebrahimy
[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 .