Global and pyramid convolutional neural network with hybrid attention mechanism for hyperspectral image classification

Figures & data

Figure 1. The GPHANet architecture for hyperspectral image classification.

Figure 2. The structure of GSSCM.

Figure 3. The structure of DPCM.

Figure 4. The structure of HAM.

Figure 5. False-colour composition and the ground truth of the PU dataset.

Figure 6. False-colour composition and the ground truth of the SA dataset.

Figure 7. False-colour composition and the ground truth of the HO dataset.

Figure 8. The impact of the patch size on GPHANet is evaluated on three datasets.

Figure 9. The impact of the principal components $b$ on GPHANet is evaluated on three datasets.

Figure 10. Visual comparison on the PU dataset: (a) Ground-truth, (b) SVM, (c) CDCNN, (d) SSRN, (e) DBDA, (f) DCRN, (g) SSFTT, (h) GPHANet.

Table 1. Classification results of seven methods on the PU dataset.

Classes	SVM	CDCNN	SSRN	DBDA	DCRN	SSFTT	GPHANet
	89.85	89.95	99.14	96.20	97.63	98.89	98.50
	92.80	97.69	99.15	99.78	99.76	99.85	99.74
	69.90	91.88	82.46	98.07	97.81	95.66	98.52
	93.96	99.58	100	99.45	98.21	98.50	98.06
	98.44	99.77	98.59	99.32	99.84	99.08	99.47
	85.00	92.48	99.89	99.51	99.47	99.73	99.90
	73.15	74.27	90.08	99.73	99.12	93.82	100
	80.69	88.95	92.10	99.05	95.33	96.22	98.50
	99.25	95.76	100	99.12	99.77	97.32	97.04
OA	88.92	94.26	97.48	98.62	98.80	98.79	99.22
AA	87.08	92.26	95.72	98.47	98.55	97.68	98.86
Kappa	85.23	92.38	96.62	98.17	98.39	98.41	98.97

Figure 11. Visual comparison on the SA dataset: (a) Ground-truth, (b) SVM, (c) CDCNN, (d) SSRN, (e) DBDA, (f) DCRNet, (g) SSFTT, (h) GPHANet.

Figure 12. Visual comparison on the HO dataset: (a) Ground-truth, (b) SVM, (c) CDCNN, (d) SSRN, (e) DBDA, (f) DCRN, (g) SSFTT, (h) GPHANet.

Table 2. Classification results of seven methods on the SA dataset.

Classes	SVM	CDCNN	SSRN	DBDA	DCRN	SSFTT	GPHANet
	99.95	99.70	99.59	100	100	99.64	100
	97.94	99.67	99.95	100	100	99.65	100
	88.60	86.78	95.45	99.38	96.47	100	99.64
	95.73	95.68	97.21	89.95	99.55	96.09	100
	95.64	98.25	99.73	99.96	98.83	97.75	98.46
	99.85	98.03	99.97	100.00	100	99.89	100
	99.32	98.92	99.49	99.80	99.43	100	100
	77.30	89.43	91.24	95.06	98.50	99.28	99.01
	99.31	99.61	99.98	99.46	99.98	100	99.95
	88.39	92.89	96.36	96.52	99.55	97.44	99.41
	81.49	84.49	90.33	93.11	98.02	100	100
	97.69	99.26	99.37	100	99.47	93.79	99.47
	94.44	99.67	98.24	99.89	99.88	99.77	99.44
	93.71	99.14	98.49	96.96	98.59	99.71	100
	75.15	82.22	83.28	94.98	92.13	94.49	93.68
	97.71	99.76	99.65	99.83	100	100	99.77
OA	89.51	93.59	96.24	97.49	98.25	98.42	98.74
AA	92.63	95.21	96.77	97.81	98.81	98.59	99.30
Kappa	88.30	92.87	94.48	97.20	98.06	98.24	98.59

Table 3. Classification results of seven methods on the HO dataset.

Classes	SVM	CDCNN	SSRN	DBDA	DCRN	SSFTT	GPHANet
	93.68	97.13	99.02	98.48	99.32	98.66	96.79
	63.81	82.68	96.06	75.01	84.57	90.51	80.52
	79.18	88.67	94.41	90.56	94.55	93.81	94.99
	92.06	98.65	98.95	99.39	99.39	99.04	99.44
	57.31	84.59	93.81	80.68	83.34	89.10	95.52
	82.58	95.14	95.56	98.84	97.52	99.01	98.59
	63.64	83.42	89.87	93.60	87.09	92.63	94.41
	26.07	48.74	70.70	81.48	83.41	85.32	88.45
	95.16	96.53	97.68	99.20	99.38	99.48	98.27
	67.81	90.02	77.76	92.02	99.08	92.41	97.07
	56.72	80.25	87.27	95.63	94.89	96.25	96.96
	56.58	62.72	67.58	94.00	89.06	81.05	87.18
	60.62	82.97	86.15	84.16	91.88	88.11	88.17
	89.47	86.20	90.22	98.04	96.86	93.20	97.43
	88.10	96.51	71.20	100.00	97.66	79.75	95.10
	78.42	89.67	93.30	89.31	99.76	94.81	99.50
	70.99	72.99	87.56	94.34	79.08	90.88	87.21
	53.35	78.29	89.99	98.56	92.37	86.31	94.88
	77.81	91.13	88.46	87.92	89.77	91.47	99.27
	68.99	88.37	88.15	95.58	97.07	86.15	93.82
	20.00	37.54	64.50	74.91	83.56	81.98	87.83
	49.59	89.57	94.95	94.84	94.39	93.10	90.16
OA	82.20	91.67	93.86	95.57	96.06	95.80	96.80
AA	70.99	82.81	87.42	91.66	92.45	91.05	93.70
Kappa	76.07	89.82	92.33	94.40	95.02	94.69	95.95

Table 4. The testing time (s) between different methods on three datasets.

Dataset	SVM	CDCNN	SSRN	DBDA	DCRN	SSFTT	GPHANet
PU	–	4.36	14.42	22.21	29.56	10.11	14.96
SA	–	6.74	23.39	36.68	43.04	18.72	26.15
HO	–	74.23	195.77	354.63	392.18	183.05	231.54

Figure 13. The effectiveness of GSSCM on three datasets, (a) PU, (b) SA, (c) HO.

Figure 14. The effectiveness of DPCM on three datasets, (a) PU, (b) SA, (c) HO.

Figure 15. The effectiveness of HAM on three datasets, (a) PU, (b) SA, (c) HO.

Figure 16. Visualization of the feature maps generated before and after HAM.

Figure 17. OA results of seven methods under different proportions of training samples: (a) PU, (b) SA, (C) HO.