Related references
Note: Only part of the references are listed.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)
Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps - A case study of North Taiwan from 1990 to 2015
Hsiao-chien Shih et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2020)
Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier
Lan H. Nguyen et al.
REMOTE SENSING OF ENVIRONMENT (2020)
Recursive Feature Elimination and Random Forest Classification of Natura 2000 Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion
Luca Demarchi et al.
REMOTE SENSING (2020)
A New Approach to High-Resolution Urban Land Use Classification Using Open Access Software and True Color Satellite Images
Fernando Chapa et al.
SUSTAINABILITY (2019)
Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping
Hsiao-chien Shih et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2019)
Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data
Yanan Liu et al.
REMOTE SENSING (2018)
Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM + imagery
Lloyd L. Coulter et al.
REMOTE SENSING OF ENVIRONMENT (2016)
Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product
Eric Vermote et al.
REMOTE SENSING OF ENVIRONMENT (2016)
Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning
Akpona Okujeni et al.
REMOTE SENSING OF ENVIRONMENT (2015)
Consistent classification of image time series with automatic adaptive signature generalization
Josh Gray et al.
REMOTE SENSING OF ENVIRONMENT (2013)
Urban Vegetation Cover and Vegetation Change in Accra, Ghana: Connection to Housing Quality
Douglas A. Stow et al.
PROFESSIONAL GEOGRAPHER (2013)
Assessing the Utility of Satellite Imagery with Differing Spatial Resolutions for Deriving Proxy Measures of Slum Presence in Accra, Ghana
Justin Stoler et al.
GISCIENCE & REMOTE SENSING (2012)
Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data
Zhe Zhu et al.
REMOTE SENSING OF ENVIRONMENT (2012)
Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas
Matheus Alves Vieira et al.
REMOTE SENSING OF ENVIRONMENT (2012)
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
Dennis C. Duro et al.
REMOTE SENSING OF ENVIRONMENT (2012)
Monitoring land cover change in urban and pen-urban areas using dense time stacks of Landsat satellite data and a data mining approach
Annemarie Schneider
REMOTE SENSING OF ENVIRONMENT (2012)
A fast separability-based feature-selection method for high-dimensional remotely sensed image classification
Baofeng Guo et al.
PATTERN RECOGNITION (2008)
Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?
B. Dixon et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2008)
Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil
Rebecca L. Powell et al.
REMOTE SENSING OF ENVIRONMENT (2007)
A Landsat surface reflectance dataset for North America, 1990-2000
JG Masek et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2006)
Support vector machines for classification in remote sensing
M Pal et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)
Normalized spectral mixture analysis for monitoring urban composition using ETM plus imagery
CS Wu
REMOTE SENSING OF ENVIRONMENT (2004)
Spatial metrics and image texture for mapping urban land use
M Herold et al.
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2003)
Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?
C Song et al.
REMOTE SENSING OF ENVIRONMENT (2001)