Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data

(2020) Miao He et al.   Remote Sensing

Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification

(2019) Christopher A. Ramezan et al.   Remote Sensing

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

(2019) Aaron E. Maxwell et al.   Remote Sensing

Implementation of machine-learning classification in remote sensing: an applied review

(2018) Aaron E. Maxwell et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery

(2018) Ming Shang et al.   Journal of the Indian Society of Remote Sensing

Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites

(2018) Shahriar S. Heydari et al.   REMOTE SENSING OF ENVIRONMENT

The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

(2018) Shichao Jin et al.   Remote Sensing

A review of supervised object-based land-cover image classification

(2017) Lei Ma et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(2017) Marvin N. Wright et al.   Journal of Statistical Software

Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review

(2017) Aaron E. Maxwell et al.   PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

(2017) Phan Thanh Noi et al.   SENSORS

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

(2017) Edwin Raczko et al.   European Journal of Remote Sensing

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

(2017) Zhen-wang LI et al.   Journal of Integrative Agriculture

Developing a Random Forest Algorithm for MODIS Global Burned Area Classification

(2017) Rubén Ramo et al.   Remote Sensing

Advanced Spectral Classifiers for Hyperspectral Images: A review

(2017) Pedram Ghamisi et al.   IEEE Geoscience and Remote Sensing Magazine

Random forest in remote sensing: A review of applications and future directions

(2016) Mariana Belgiu et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research

(2016) Reza Khatami et al.   REMOTE SENSING OF ENVIRONMENT

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

The AmericaView classification methods accuracy comparison project: A rigorous approach for model selection

(2015) Rick L. Lawrence et al.   REMOTE SENSING OF ENVIRONMENT

Urban land cover classification using airborne LiDAR data: A review

(2015) Wai Yeung Yan et al.   REMOTE SENSING OF ENVIRONMENT

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

(2015) Koreen Millard et al.   Remote Sensing

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

(2015) Jordi Inglada et al.   Remote Sensing

Automated parameterisation for multi-scale image segmentation on multiple layers

(2014) L. Drăguţ et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

(2014) F.E. Fassnacht et al.   REMOTE SENSING OF ENVIRONMENT

Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

(2014) Yuguo Qian et al.   Remote Sensing

Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective

(2013) Damien Arvor et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery

(2013) Minho Kim et al.   PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING

Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data

(2012) Bahram Salehi et al.   Remote Sensing

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

(2012) Markus Immitzer et al.   Remote Sensing

Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects

(2011) Minho Kim et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data

(2010) Lucian Drǎguţ et al.   INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

Support vector machines in remote sensing: A review

(2010) Giorgos Mountrakis et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Object based image analysis for remote sensing

(2009) T. Blaschke   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

(2009) Luis Samaniego et al.   Remote Sensing