Hyperspectral Remote Sensing Classification Using Multi-Scale Adaptive Capsule Network

Class No.	Land cover	Training	Validation	Test
1	Asphalt	1000	1000	4631
2	Meadows	1000	1001	16650
3	Gravel	460	461	1180
4	Trees	890	891	1285
5	Painted metal sheets	400	401	546
6	Bare Soil	1000	1001	3030
7	Bitumen	400	401	531
8	Self-Blocking Bricks	1000	1001	1683
9	Shadows	260	261	428

Table 2. Number of training, verification, and test samples of various types of objects on SA dataset

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Table 2. Number of training, verification, and test samples of various types of objects on SA dataset

Class No.	Land cover	Training	Validation	Test
1	Brocoli_green_weeds_1	100	100	191
2	Corn_senesced_green_weeds	390	390	563
3	Lettuce_romaine_4wk	150	150	316
4	Lettuce_romaine_5wk	470	470	585
5	Lettuce_romaine_6wk	210	210	254
6	Lettuce_romaine_7wk	250	250	299

Table 3. Accuracy, overall accuracy, Kappa coefficient (K), and p-value of different algorithms on PU dataset

View table

Table 3. Accuracy, overall accuracy, Kappa coefficient (K), and p-value of different algorithms on PU dataset

No.	Accuracy /%
No.	SVM	RF	PCA-SVM	PCA-RF	CNN	CapsNet	MCaps	ARWI-Caps	MSCNN	MSCaps
1	99.21	98.89	99.38	97.56	99.37	98.05	99.09	99.42	99.44	99.49
2	98.99	98.32	98.87	98.24	99.78	99.96	99.93	99.97	99.51	99.97
3	76.32	79.94	76.83	89.77	76.39	95.35	96.19	98.49	98.45	98.31
4	95.91	89.58	95.86	87.10	98.96	92.76	95.46	95.77	99.74	97.15
5	99.85	99.48	99.85	99.85	99.93	96.13	98.82	99.70	99.56	99.85
6	90.12	81.42	89.86	82.98	98.13	98.45	99.41	99.83	99.23	99.84
7	92.38	88.35	92.52	96.38	98.30	99.33	99.25	99.89	98.81	99.92
8	93.39	91.31	93.45	89.68	97.64	97.88	99.48	99.51	98.17	99.62
9	100	99.79	100	99.89	99.58	97.15	97.95	99.68	98.03	99.89
OA /%	91.60	87.38	91.86	88.44	95.84	96.73	98.22	98.96	98.71	99.14
K	0.82	0.74	0.84	0.76	0.91	0.93	0.96	0.97	0.97	0.99
p	$≪ 0.05$	0.0	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	0.011	1.501×10^-5

Table 4. Accuracy, overall accuracy, Kappa coefficient (K), and p-value of different algorithms on SA dataset

View table

Table 4. Accuracy, overall accuracy, Kappa coefficient (K), and p-value of different algorithms on SA dataset

No.	Accuracy /%
No.	SVM	RF	PCA-SVM	PCA-RF	CNN	CapsNet	MCaps	ARWI-Caps	MSCNN	MSCaps
1	100	100	100	100	99.74	100	98.73	96.54	100	100
2	100	97.08	99.26	96.99	93.26	100	100	100	100	100
3	99.84	99.83	99.88	100	93.92	100	100	100	100	100
4	99.35	99.09	99.35	98.58	99.08	90.55	99.74	99.87	99.54	99.87
5	87.53	65.31	86.86	71.17	93.48	98.39	89.99	96.70	96.70	100
6	72.70	80.75	73.64	92.26	84.36	87.71	89.92	92.15	88.96	88.88
OA/%	81.57	73.41	82.13	82.21	85.01	87.27	92.16	95.10	94.07	95.38
K	0.66	0.52	0.69	0.67	0.72	0.76	0.85	0.90	0.89	0.91
p	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	$≪ 0.05$	0.033	0.025

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Gen Zhang, Xiaohui Ding, Ji Yang, Hua Wang. Hyperspectral Remote Sensing Classification Using Multi-Scale Adaptive Capsule Network[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2428004

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Paper Information

Category: Remote Sensing and Sensors

Received: Sep. 7, 2021

Accepted: Nov. 2, 2021

Published Online: Nov. 28, 2022

The Author Email: Xiaohui Ding (dxh2017@sina.com)

DOI:10.3788/LOP202259.2428004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology