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

View table

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: Ding Xiaohui (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