On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping

Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation

(2015) A.E. Maxwell et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey

(2015) Ö. Akar et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

A Semi-Automated, Multi-Source Data Fusion Update of a Wetland Inventory for East-Central Minnesota, USA

(2015) Steven M. Kloiber et al.   WETLANDS

The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands

(2015) Jennifer Corcoran et al.   Remote Sensing

Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X

(2014) Rei Sonobe et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

(2014) Aleksi Räsänen et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Potential contributions of remote sensing to ecosystem service assessments

(2014) Margaret E. Andrew et al.   PROGRESS IN PHYSICAL GEOGRAPHY

A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost

(2014) L. Chasmer et al.   REMOTE SENSING OF ENVIRONMENT

Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR–SAR fusion using a random forest classifier

(2013) Koreen Millard et al.   CANADIAN JOURNAL OF REMOTE SENSING

Active Learning in the Spatial Domain for Remote Sensing Image Classification

(2013) Andre Stumpf et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Object-oriented mapping of urban trees using Random Forest classifiers

(2013) Anne Puissant et al.   International Journal of Applied Earth Observation and Geoinformation

Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification

(2013) Zhen Zhen et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

(2013) Jennifer Corcoran et al.   Remote Sensing

Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP

(2011) E. M. Adam et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery

(2011) Dennis C. Duro et al.   REMOTE SENSING OF ENVIRONMENT

Using topographic wetness index in vegetation ecology: does the algorithm matter?

(2010) Martin Kopecký et al.   APPLIED VEGETATION SCIENCE

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

(2010) Johannes Breidenbach et al.   REMOTE SENSING OF ENVIRONMENT

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING