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Table 1 Classification accuracy on the Indian Pines dataset (10% samples for training)

From: Hyperspectral image classification with SVM and guided filter

 

SVM

SVM-EPF

Co-SVM

Co-SVM-EPF

GF-SVM

GF-SVM-EPF

Alfalfa

83.33

100.0

100.0

100.0

91.30

100.0

Corn-N

76.35

94.22

96.21

95.74

98.03

99.16

Corn-M

71.74

96.66

95.93

95.80

98.42

99.82

Corn

48.68

78.97

92.22

91.26

89.34

94.83

Grass-M

87.38

97.53

99.25

99.49

96.68

99.26

Grass-T

95.06

99.24

100.0

100.0

97.94

99.17

Grass-P-M

86.67

100.0

100.0

100.0

92.31

100.0

Hay-W

99.02

100.0

100.0

100.0

100.0

100.0

Oats

52.63

100.0

100.0

100.0

76.92

100.0

Soybean-N

75.38

85.07

93.27

95.58

95.86

98.73

Soybean-M

83.98

95.84

94.45

96.04

99.20

99.41

Soybean-C

71.12

94.45

98.63

97.89

96.24

99.45

Wheat

93.75

100.0

100.0

100.0

98.90

100.0

Woods

94.27

98.62

100.0

99.24

100.0

99.58

Build-G-T-D

59.78

85.31

96.02

96.96

97.77

97.25

Stone-S-T

86.79

92.16

100.0

97.62

95.35

95.45

OA

81.02

94.57

96.63

97.07

98.12

99.22

AA

79.12

94.88

97.87

97.85

95.27

98.88

KA

78.29

93.75

96.11

96.62

97.80

99.08