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

Table 5

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

Class	VGG-16	Inception-v3	ResNet-50	Proposed
Water	99.89	100	100	100
Trees	94.85	95.78	95.10	95.43
Asphalt	93.90	96.39	96.02	95.18
Self-blocking bricks	87.56	89.08	90.17	90.38
Bitumen	95.44	96.71	96.50	97.84
Tiles	96.34	98.58	98.49	98.95
Shadows	95.04	95.20	94.99	95.46
Meadows	97.63	98.05	98.57	99.65
Bare soil	96.50	96.89	98.17	99.68
Overall accuracy	96.93	97.90	97.55	98.94