Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network

Li Hu; Rui Shan; Fang Wang; Guoqian Jiang; Jingyi Zhao; Zhi Zhang

doi:10.3788/LOP57.122803

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 12 >Page 122803 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 12, 122803 (2020)

Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network

Li Hu¹, Rui Shan¹, Fang Wang¹, Guoqian Jiang², Jingyi Zhao^3、*, and Zhi Zhang⁴

Author Affiliations

¹School of Science, Yanshan University, Qinhuangdao, Hebei 0 66001, China

²School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 0 66001, China

³School of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei 0 66001, China

⁴Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China;

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DOI: 10.3788/LOP57.122803 Cite this Article Set citation alerts

Li Hu, Rui Shan, Fang Wang, Guoqian Jiang, Jingyi Zhao, Zhi Zhang. Hyperspectral Image Classification Based on Dual-Channel Dilated Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122803 Copy Citation Text

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Fig. 1. Structure diagram of the proposed framework

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Fig. 2. Schematic of 1D dilated convolution. (a) Standard convolution; (b) dilated convolution

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Fig. 3. DCD-CNN structure

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Fig. 4. OA accuracy of different datasets at different λ values

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Fig. 5. Classification maps of different methods on Indian Pines dataset. (a) Ground truth; (b) SVM; (c) AEAP; (d) DCNN; (e) FEFCN-ELM; (f) proposed method

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Fig. 6. Classification maps of different methods on Pavia University dataset. (a) Ground truth; (b) SVM; (c) AEAP; (d) DCNN; (e) FEFCN-ELM; (f) proposed method

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Class	SVM	AEAP	DCNN	FEFCN-ELM	Proposed
1	21.73	67.39	91.30	91.30	98.91
2	64.07	81.86	90.12	95.37	98.76
3	62.89	73.73	77.71	92.53	99.55
4	51.47	79.32	55.27	85.23	96.85
5	83.02	94.82	88.40	92.13	97.05
6	96.30	98.90	97.53	99.17	99.79
7	64.28	89.28	82.14	82.14	91.07
8	97.07	97.90	98.74	99.58	100.00
9	45.00	65.00	45.00	45.00	60.00
10	71.70	79.42	90.53	95.78	97.25
11	85.09	90.87	97.10	98.20	99.67
12	70.65	80.26	89.03	97.13	98.27
13	96.58	99.02	100.00	99.89	100.00
14	95.65	97.86	97.15	98.81	99.68
15	62.95	62.69	73.57	91.70	99.03
16	82.79	91.39	70.96	82.79	87.37
OA	79.00	87.15	90.97	96.16	98.83
AA	71.95	84.36	84.03	90.43	95.20
Kappa	75.94	85.28	89.65	95.61	98.66

Table 1. Classification results of different methods on Indian Pines datasetunit: %

Class	SVM	AEAP	DCNN	FEFCN-ELM	Proposed
1	84.05	92.51	94.40	96.24	99.95
2	94.90	97.75	98.52	98.12	99.99
3	55.93	82.46	80.56	81.65	100.00
4	66.74	94.64	90.79	94.81	98.21
5	88.02	98.43	98.88	97.47	100.00
6	70.07	84.21	86.41	80.53	100.00
7	86.01	83.08	82.78	91.95	100.00
8	90.76	83.43	88.91	93.07	99.45
9	99.68	100.00	99.89	99.68	100.00
OA	85.63	92.75	93.75	94.10	99.82
AA	81.80	90.72	91.24	92.61	99.73
Kappa	80.65	90.36	91.65	92.13	99.75

Table 2. Classification results of different methods on Pavia University datasetunit: %

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