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OBJECT-BASED RANDOM FOREST CLASSIFICATION OF LAND COVER FROM REMOTELY SENSED IMAGERY FOR INDUSTRIAL AND MINING RECLAMATION
Reclamation Area Classification of Land Use Random Forest Grid-search Object-based Multi-resolution Segmentation Multi-feature Variables
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2018/5/11
The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification ...
APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI
Classification Landsat 8 OLI Land use Land cover Random Forest Decision Tree
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2018/4/18
This study used the Random Forest classifier (RF) running in R environment to map Land use/Land cover (LULC) of Dak Lak province in Vietnam based on the Landsat 8 OLI. The values of two RF parameters ...
APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI
Classification Landsat 8 OLI Land use Land cover Random Forest Decision Tree
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2018/5/8
This study used the Random Forest classifier (RF) running in R environment to map Land use/Land cover (LULC) of Dak Lak province in Vietnam based on the Landsat 8 OLI. The values of two RF parameters ...
RANDOM FOREST CLASSIFICATION OF SEDIMENTS ON EXPOSED INTERTIDAL FLATS USING ALOS-2 QUAD-POLARIMETRIC SAR DATA
Coastal Zones Surveillance SAR Polarimetric Decomposition Optical Channels Random Forest
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2016/12/1
Coastal zones are one of the world’s most densely populated areas and it is necessary to propose an accurate, cost effective, frequent, and synoptic method of monitoring these complex ecosystems. Howe...
MERGING RANDOM FOREST CLASSIFICATION WITH AN OBJECT-ORIENTED APPROACH FOR ANALYSIS OF AGRICULTURAL LANDS
land use agriculture Landsat imagery segmentation Random Forest
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2015/12/31
Machine learning algorithms recently have made major advances, with decision tree classifiers gaining wide acceptance. Boosting and bagging of decision trees have added to the predictive capabilities ...
DETERMINATION OF OPTIMAL SCALE PARAMETER FOR ALLIANCE-LEVEL FOREST CLASSIFICATION OF MULTISPECTRAL IKONOS IMAGES
Object-based Classification Optimal Scale Parameter Graphs of Local Variance Multispectral Ikonos Image
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2015/8/19
The automated interpretation of remotely sensed data is still performed largely on the basis of per-pixel image classification, i.e., the statistical analysis of each pixel’s spectral value. The conve...
Forest above ground biomass estimation and forest/non-forest classification for Odisha, India, using L-band Synthetic Aperture Radar (SAR) data
Above ground biomass SAR backscatter coefficient Texture Support Vector Machine Classification
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2014/12/15
Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass over...
ASSESSING THE SIGNIFICANCE OF HYPERION SPECTRAL BANDS IN FOREST CLASSIFICATION
Forest Classification Hyperspectral Ensemble Decision Tree Random Forests
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2014/4/29
The classification of vegetation in hyperspectral image scenes presents some challenges due to high band autocorrelations and problems dealing with many predictor variables. The Random Forests classif...