Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Table 4

HSI categorization for the Houston U dataset.

Methods	RNN	DCNN	SDCNN	CSDCNN	CSDCNN-AO
OA	49.23 ± 1.45	57.49 ± 1.39	88.73 ± 1.13	93.78 ± 0.86	94.67 ± 1.08
AA	78.20 ± 1.06	49.60 ± 3.29	88.46 ± 1.17	83.14 ± 1.11	94.44 ± 1.82
K	53.97 ± 0.58	51.04 ± 1.03	89.62 ± 2.54	89.13 ± 0.26	97.33 ± 1.25
1	27.89 ± 1.09	1.66 ± 7.33	92.00 ± 1.03	37.21 ± 30.0	94.77 ± 11.6
2	49.46 ± 5.00	42.87 ± 3.04	89.79 ± 2.80	82.69 ± 0.26	92.56 ± 4.87
3	26.69 ± 2.61	30.91 ± 8.28	87.18 ± 7.24	75.93 ± 1.26	90.06 ± 4.53
4	22.79 ± 9.7	21.17 ± 3.25	83.17 ± 5.52	89.11 ± 1.12	96.84 ± 4.89
5	37.71 ± 6.67	69.79 ± 2.13	86.75 ± 2.55	79.28 ± 1.34	95.65 ± 1.95
6	88.79 ± 1.71	92.78 ± 0.78	88.08 ± 3.06	93.82 ± 0.32	98.95 ± 0.96
7	38.54 ± 11.4	20.85 ± 7.59	72.89 ± 29.7	40.69 ± 2.13	92.48 ± 24.0
8	89.96 ± 2.15	88.84 ± 3.15	84.25 ± 2.40	99.33 ± 0.31	86.11 ± 3.09
9	57.81 ± 19.04	0.00 ± 0.00	76.0 ± 49.0	28.00 ± 2.05	99.72 ± 8.38
10	67.46 ± 1.91	67.53 ± 1.24	90.47 ± 6.87	87.28 ± 0.89	95.30 ± 2.86
11	82.56 ± 3.49	73.88 ± 4.33	94.57 ± 5.03	95.21 ± 0.23	99.84 ± 3.29
12	54.14 ± 5.56	48.46 ± 3.85	77.47 ± 5.16	92.77 ± 3.37	92.56 ± 3.44
13	34.47 ± 7.17	83.02 ± 1.22	97.26 ± 5.29	49.93 ± 3.56	97.89 ± 0.87
14	84.32 ± 8.95	93.94 ± 2.59	89.16 ± 2.22	95.89 ± 1.37	95.89 ± 2.57
15	48.75 ± 5.12	47.64 ± 4.56	98.00 ± 7.49	96.77 ± 2.98	93.74 ± 2.65