Hyperspectral Image Classification Model Using Squeeze and Excitation Network with Deep Learning

Table 6

Evaluation of accuracy on each class of Pavia University dataset with overall accuracy.

Class	VGG-16	Inception-v3	ResNet-50	Proposed
Asphalt	93.85	92.61	96.20	96.89
Meadows	96.04	96.35	97.52	98.74
Gravel	78.33	81.26	80.09	81.63
Trees	90.12	96.79	96.62	96.08
Painted metal sheets	99.90	100	99.85	100
Bare soil	89.44	91.73	94.26	93.38
Bitumen	85.60	90.83	86.90	89.16
Self-blocking bricks	86.75	89.48	92.07	92.94
Shadows	100	99.73	99.97	100
Overall accuracy	94.85	95.14	95.57	96.05