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  "author": "Valdivieso-Ros, C., Alonso-Sarria, F. y Gomariz-Castillo 
            
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  "author_name": "Valdivieso-Ros, C., Alonso-Sarria, F. y 
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      "name": "Valdivieso-Ros, C., Alonso-Sarria, F. y 
            
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Gomariz-Castillo F."
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  "notes": "Land cover classification in semiarid areas is a difficult 
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  "notes": "Land cover classification in semiarid areas is a difficult 
            
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task that has been tackled using different strategies, such as the use 
94
task that has been tackled using different strategies, such as the use 
            
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of normalized indices, texture metrics, and the combination of images 
95
of normalized indices, texture metrics, and the combination of images 
            
96
from different dates or different sensors. In this paper we present 
96
from different dates or different sensors. In this paper we present 
            
97
the results of an experiment using three sensors (Sentinel-1 SAR, 
97
the results of an experiment using three sensors (Sentinel-1 SAR, 
            
98
Sentinel-2 MSI and LiDAR), four dates and different normalized indices 
98
Sentinel-2 MSI and LiDAR), four dates and different normalized indices 
            
99
and texture metrics to classify a semiarid area. Three machine 
99
and texture metrics to classify a semiarid area. Three machine 
            
100
learning algorithms were used: Random Forest, Support Vector Machines 
100
learning algorithms were used: Random Forest, Support Vector Machines 
            
101
and Multilayer Perceptron; Maximum Likelihood was used as a baseline 
101
and Multilayer Perceptron; Maximum Likelihood was used as a baseline 
            
102
classifier. The synergetic use of all these sources resulted in a 
102
classifier. The synergetic use of all these sources resulted in a 
            
103
significant increase in accuracy, Random Forest being the model 
103
significant increase in accuracy, Random Forest being the model 
            
104
reaching the highest accuracy. However, the large amount of features 
104
reaching the highest accuracy. However, the large amount of features 
            
105
(126) advises the use of feature selection to reduce this figure. 
105
(126) advises the use of feature selection to reduce this figure. 
            
106
After using Variance Inflation Factor and Random Forest feature 
106
After using Variance Inflation Factor and Random Forest feature 
            
107
importance, the amount of features was reduced to 62. The final 
107
importance, the amount of features was reduced to 62. The final 
            
108
overall accuracy obtained was 0.91 \u00b1 0.005 (\ud835\udefc = 0.05) 
108
overall accuracy obtained was 0.91 \u00b1 0.005 (\ud835\udefc = 0.05) 
            
109
and kappa index 0.898 \u00b1 0.006 (\ud835\udefc = 0.05). Most of the 
109
and kappa index 0.898 \u00b1 0.006 (\ud835\udefc = 0.05). Most of the 
            
110
observed confusions are easily explicable and do not represent a 
110
observed confusions are easily explicable and do not represent a 
            
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significant difference in agronomic terms.",
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significant difference in agronomic terms.",
            
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  "notes_translated": {
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  "notes_translated": {
            
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    "es": "Land cover classification in semiarid areas is a difficult 
113
    "es": "Land cover classification in semiarid areas is a difficult 
            
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task that has been tackled using different strategies, such as the use 
114
task that has been tackled using different strategies, such as the use 
            
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of normalized indices, texture metrics, and the combination of images 
115
of normalized indices, texture metrics, and the combination of images 
            
116
from different dates or different sensors. In this paper we present 
116
from different dates or different sensors. In this paper we present 
            
117
the results of an experiment using three sensors (Sentinel-1 SAR, 
117
the results of an experiment using three sensors (Sentinel-1 SAR, 
            
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Sentinel-2 MSI and LiDAR), four dates and different normalized indices 
118
Sentinel-2 MSI and LiDAR), four dates and different normalized indices 
            
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and texture metrics to classify a semiarid area. Three machine 
119
and texture metrics to classify a semiarid area. Three machine 
            
120
learning algorithms were used: Random Forest, Support Vector Machines 
120
learning algorithms were used: Random Forest, Support Vector Machines 
            
121
and Multilayer Perceptron; Maximum Likelihood was used as a baseline 
121
and Multilayer Perceptron; Maximum Likelihood was used as a baseline 
            
122
classifier. The synergetic use of all these sources resulted in a 
122
classifier. The synergetic use of all these sources resulted in a 
            
123
significant increase in accuracy, Random Forest being the model 
123
significant increase in accuracy, Random Forest being the model 
            
124
reaching the highest accuracy. However, the large amount of features 
124
reaching the highest accuracy. However, the large amount of features 
            
125
(126) advises the use of feature selection to reduce this figure. 
125
(126) advises the use of feature selection to reduce this figure. 
            
126
After using Variance Inflation Factor and Random Forest feature 
126
After using Variance Inflation Factor and Random Forest feature 
            
127
importance, the amount of features was reduced to 62. The final 
127
importance, the amount of features was reduced to 62. The final 
            
128
overall accuracy obtained was 0.91 \u00b1 0.005 (\ud835\udefc = 0.05) 
128
overall accuracy obtained was 0.91 \u00b1 0.005 (\ud835\udefc = 0.05) 
            
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and kappa index 0.898 \u00b1 0.006 (\ud835\udefc = 0.05). Most of the 
129
and kappa index 0.898 \u00b1 0.006 (\ud835\udefc = 0.05). Most of the 
            
130
observed confusions are easily explicable and do not represent a 
130
observed confusions are easily explicable and do not represent a 
            
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significant difference in agronomic terms."
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significant difference in agronomic terms."
            
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