Related references
Note: Only part of the references are listed.Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery
Rashmi Saini et al.
GEOCARTO INTERNATIONAL (2021)
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
Abdulhakim Mohamed Abdi
GISCIENCE & REMOTE SENSING (2020)
Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests
Sruthi M. Krishna Moorthy et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)
In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data
Subhadip Dey et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2020)
Dilated multi-scale cascade forest for satellite image classification
Min Xia et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2020)
Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles
Alim Samat et al.
REMOTE SENSING (2020)
Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery
Mateo Gasparovic et al.
REMOTE SENSING (2020)
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland
M. Mahdianpari et al.
GISCIENCE & REMOTE SENSING (2020)
Accelerated gradient boosting
G. Biau et al.
MACHINE LEARNING (2019)
Deep learning based multi-temporal crop classification
Liheng Zhong et al.
REMOTE SENSING OF ENVIRONMENT (2019)
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
Mustafa Ustuner et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China
Hao Jiang et al.
REMOTE SENSING (2019)
Context Aggregation Network for Semantic Labeling in Aerial Images
Wensheng Cheng et al.
REMOTE SENSING (2019)
Deep Learning for Classification of Hyperspectral Data
Nicolas Audebert et al.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2019)
An Unsupervised Binary and Multiple Change Detection Approach for Hyperspectral Imagery Based on Spectral Unmixing
Hamid Jafarzadeh et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2019)
Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application
Stefanos Georganos et al.
GISCIENCE & REMOTE SENSING (2018)
Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images
Gebrejewergs Aredehey et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)
Implementation of machine-learning classification in remote sensing: an applied review
Aaron E. Maxwell et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)
Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
Feng Gao et al.
REMOTE SENSING (2018)
Spectral-Spatial Semisupervised Hyperspectral Classification Using Adaptive Neighborhood
Nasehe Jamshidpour et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)
Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015
Peng Dou et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)
Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique
Yangbo Chen et al.
REMOTE SENSING (2017)
Scene Classification via a Gradient Boosting Random Convolutional Network Framework
Fan Zhang et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)
Random forest in remote sensing: A review of applications and future directions
Mariana Belgiu et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)
Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers
Artur Nowakowski
ACTA GEOPHYSICA (2015)
The application of ensemble techniques for land-cover classification in arid lands
Marwa Waseem A. Halmy et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2015)
Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy)
L. Lombardo et al.
NATURAL HAZARDS (2015)
Object-based classification with rotation forest ensemble learning algorithm using very-high-resolution WorldView-2 image
Taskin Kavzoglu et al.
REMOTE SENSING LETTERS (2015)
Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers
Artur Nowakowski
ACTA GEOPHYSICA (2015)
Integrating Color Features in Polarimetric SAR Image Classification
Stefan Uhlmann et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2014)
Decision tree approach for classification of remotely sensed satellite data using open source support
Richa Sharma et al.
JOURNAL OF EARTH SYSTEM SCIENCE (2013)
Gradient boosting machines, a tutorial
Alexey Natekin et al.
FRONTIERS IN NEUROROBOTICS (2013)
Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data
Xin Miao et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2012)
Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems
Anindya Halder et al.
APPLIED SOFT COMPUTING (2011)
The DGPF-Test on Digital Airborne Camera Evaluation - Overview and Test Design
Michael Cramer
PHOTOGRAMMETRIE FERNERKUNDUNG GEOINFORMATION (2010)
A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images
Ujjwal Maulik et al.
FUNDAMENTA INFORMATICAE (2010)
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
Jonathan Cheung-Wai Chan et al.
REMOTE SENSING OF ENVIRONMENT (2008)
Random forests for classification in ecology
D. Richard Cutler et al.
ECOLOGY (2007)
An assessment of the effectiveness of decision tree methods for land cover classification
M Pal et al.
REMOTE SENSING OF ENVIRONMENT (2003)
Multiple classifiers applied to multisource remote sensing data
GJ Briem et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2002)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
TG Dietterich
MACHINE LEARNING (2000)