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Table 9 Detailed ANOVA Single Factor p-value results between all feature reduction methods as shown in Table 3. With the accepted \(\alpha\) = 0.05, all p-values are well under the alpha, and therefore the tests are statistically significant

From: CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering

ANOVA

OA

AA

Kappa

OA

AA

Kappa

 

IP (k = 2)

Salinas (k = 3)

p-value (\(\alpha\) = 0.05)

4.75E−22

5.88E−23

3.31E−23

6.28E−18

6.67E−16

1.48E−18

 

KSC (k = 2)

Botswana (k = 3)

p-value (\(\alpha\) = 0.05)

3.96E−14

8.50E−17

3.95E−15

3.74E−13

2.87E−13

8.93E−14

 

PU (k = 2)

Houston (k = 2)

p-value (\(\alpha\) = 0.05)

5.73E−20

3.28E−22

1.11E−22

1.56E−22

1.32E−21

7.11E−23