Land cover mapping based on random forest classification of multitemporal spectral and thermal images

2015 ◽  
Vol 187 (5) ◽  
Author(s):  
Vahid Eisavi ◽  
Saeid Homayouni ◽  
Ahmad Maleknezhad Yazdi ◽  
Abbas Alimohammadi
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Thermal Images ◽  
Random Forest Classification ◽  
Forest Classification

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

2012 ◽  
Vol 121 ◽  
pp. 93-107 ◽  
Author(s):  
V.F. Rodriguez-Galiano ◽  
M. Chica-Olmo ◽  
F. Abarca-Hernandez ◽  
P.M. Atkinson ◽  
C. Jeganathan
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries

2015 ◽  
Vol 24 ◽  
pp. 215-221 ◽  
Author(s):  
Romie Jhonnerie ◽  
Vincentius P. Siregar ◽  
Bisman Nababan ◽  
Lilik Budi Prasetyo ◽  
Sam Wouthuyzen
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Alos Palsar ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Landsat 5 Tm

scholarly journals RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Mulia Inda Rahayu ◽  
Katmoko Ari Sambodo
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Land Cover Classification ◽  
Alos Palsar ◽  
Satellite Imaging ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Tropical Areas ◽  
Data Source

Recently, Synthetic Aperture Radar (SAR) satellite imaging has become an increasing popular data source especially for land cover mapping because its sensor can penetrate clouds, haze, and smoke which a serious problem for optical satellite sensor observations in the tropical areas. The objective of this study was to determine an alternative method for land cover classification of ALOS-PALSAR data using Random Forest (RF) classifier. RF is a combination (ensemble) of tree predictors that each tree predictor depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. In this paper, the performance of the RF classifier for land cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual polarization) in the area of Jambi and South Sumatra, Indonesia. Overall accuracy of this method was 88.93%, with producer’s accuracies for forest, rubber, mangrove & shrubs with trees, cropland, and water classes were greater than 92%.

Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

2014 ◽  
Vol 5 (2) ◽  
pp. 157-164 ◽  
Author(s):  
Rei Sonobe ◽  
Hiroshi Tani ◽  
Xiufeng Wang ◽  
Nobuyuki Kobayashi ◽  
Hideki Shimamura
Keyword(s):  
Random Forest ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Crop Type ◽  
Multi Temporal
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