Object-based random forest classification for detecting plastic-mulched landcover from Gaofen-2 and Landsat-8 OLI fused data

2019 ◽  
Author(s):  
Chuang Wang ◽  
Lizhen Lu
Keyword(s):  
Random Forest ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Object Based

scholarly journals AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 213-217 ◽  
Author(s):  
Hu Ding ◽  
Fei Tao ◽  
Wufan Zhao ◽  
Jiaming Na ◽  
Guo’an Tang
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Unsupervised Classification ◽  
Entropy Method ◽  
Random Forest Classification ◽  
Landform Classification ◽  
Forest Classification ◽  
Object Based ◽  
Importance Analysis ◽  
Occurrence Matrix

Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

scholarly journals APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 363-367 ◽  
Author(s):  
H. T. T. Nguyen ◽  
T. M. Doan ◽  
V. Radeloff
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Deciduous Forest ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Plantation Forest ◽  
Barren Land ◽  
Random Forest Classification

<p><strong>Abstract.</strong> 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 of ntree (number of tree) and mtry (the number of variables used to split at each node) were tested and compared. In current study the best results indicate the number of suitable decision trees involved in the classification process is 300 (ntree), and the suitable number of variables used to split at each node is 4 variables (mtry). These parameters were used to classify 7 bands multi-spectral resolution from 1&amp;ndash;7 of Landsat 8 into ten classes of LULC including natural broad-leaved evergreen, semi-evergreen, dipterocarp deciduous forest, plantation forest, rubber, coffee land, crop land, barren land, residential area and water surface. The overall accuracy of 90.32&amp;thinsp;% with Kappa coefficient of 0.8434 was found in this case.</p>

scholarly journals AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 213-217
Author(s):  
Hu Ding ◽  
Fei Tao ◽  
Wufan Zhao ◽  
Jiaming Na ◽  
Guo’an Tang
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Unsupervised Classification ◽  
Entropy Method ◽  
Random Forest Classification ◽  
Landform Classification ◽  
Forest Classification ◽  
Object Based ◽  
Importance Analysis ◽  
Occurrence Matrix

Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

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