Hyperspectral Images Classification Method Based on 3D Octave Convolution and Bi-RNN Attention Network

Lianhui LIANG; Jun LI; Shaoquan ZHANG

doi:10.3788/gzxb20215009.0910001

Acta Photonica Sinica, Volume. 50, Issue 9, 0910001(2021)

Hyperspectral Images Classification Method Based on 3D Octave Convolution and Bi-RNN Attention Network

Lianhui LIANG¹, Jun LI², and Shaoquan ZHANG^1、*

Author Affiliations

¹College of Electrical and Information Engineering， Hunan University， Changsha40082， China

²School of Geography and Planning， Sun Yat-sen University， Guangzhou51075， China

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Figures & Tables(18)

Fig. 1. Flow of framework for 3D Octave convolution and Bi-RNN attention network

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Fig. 2. Flow of 3D Octave convolution network

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Fig. 3. Structure of Bi-RNN network

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Fig. 4. Structure of Bi-RNN attention network

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Fig. 5. The whole structure diagram of 3D Octave convolution and Bi-RNN attention network

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Fig. 6. Classification maps of different methods on the Pavia University dataset

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Fig. 7. Partial enlargement comparison of classification maps on the Pavia University dataset

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Fig. 8. Classification maps of different methods on the Botswana dataset

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Fig. 9. Partial enlargement comparison of classification maps on the Botswana dataset

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Table 1. Number of training and testing samples of the Pavia University dataset

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Table 1. Number of training and testing samples of the Pavia University dataset

Code	Class name	Train	Test	Total
Total		3 930	38 846	42 776
1	Asphalt	548	6 083	6 631
2	Meadows	540	18 109	18 649
3	Gravel	392	1 707	2 099
4	Trees	542	2 522	3 064
5	Painted metal sheets	256	1 089	1 345
6	Bare soil	532	4 497	5 029
7	Bitumen	375	955	1 330
8	Self-Blocking bricks	514	3 168	3 682
9	Shadows	231	716	947

Table 2. Number of training and testing samples of the Botswana dataset

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Table 2. Number of training and testing samples of the Botswana dataset

Code	Class name	Train	Test	Total
Total		420	2 828	3 248
1	Water	30	240	270
2	Hippo grass	30	71	101
3	Floodplain grasses1	30	221	251
4	Floodplain grasses1	30	185	215
5	Reeds1	30	239	269
6	Riparian	30	239	269
7	Firescar2	30	229	259
8	Island interior	30	173	203
9	Acacia woodlands	30	284	314
10	Acacia shrublands	30	218	248
11	Acacia grasslands	30	275	305
12	Short mopane	30	151	181
13	Mixed mopane	30	238	268
14	Exposed soils	30	65	95

Table 3. Classification accuracy of different spatial size
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Table 3. Classification accuracy of different spatial size
Spatial size
11 $\times$ 11 13 $\times$ 13 15 $\times$ 15 17 $\times$ 17 19 $\times$ 19 21 $\times$ 21
Accuracy OA /% 99.86 99.93 99.97 99.96 99.96 99.91

Table 4. Classification accuracy of different dropout
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Table 4. Classification accuracy of different dropout
Dropout
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Accuracy OA /% 99.82 99.87 99.91 99.94 99.97 99.96 99.96 99.95

Table 5. Classification performance of different methods on Pavia University dataset

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Table 5. Classification performance of different methods on Pavia University dataset

Class	SVM	DBMA	ARNN	SSAN	3DOC-SSAN	3DOC-RNN
1	89.33	99.98	98.93	99.28	99.82	99.93
2	93.84	99.96	96.82	98.66	99.93	99.99
3	85.82	99.91	97.71	98.53	99.67	99.88
4	97.86	99.18	99.72	98.66	99.81	99.96
5	98.99	99.90	100.00	100.00	100.00	100.00
6	94.95	99.30	99.84	99.40	100.00	100.00
7	94.14	99.92	99.69	99.79	99.76	100.00
8	89.96	96.82	99.72	99.75	99.80	99.94
9	99.98	99.33	100.00	99.72	100.00	100.00
OA/%	93.12	99.52	98.23	99.00	99.87	99.97
AA/%	90.70	99.37	99.17	99.30	99.87	99.96
Kappa/%	93.88	99.35	97.59	98.64	99.94	99.96

Table 6. Comparison with other methods in the classification accuracy difference on Botswana dataset
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Table 6. Comparison with other methods in the classification accuracy difference on Botswana dataset
Class SVM DBMA ARNN SSAN 3DOC-SSAN
OA/% +6.85 +0.45 +1.74 +0.97 +0.10
AA/% +9.26 +0.59 +0.79 +0.66 +0.09
Kappa/% +6.08 +0.61 +1.47 +2.37 +0.02

Table 7. Classification performance of different methods

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Table 7. Classification performance of different methods

Class	SVM	DBMA	ARNN	SSAN	3DOC-SSAN	3DOC-RNN
1	100.00	98.94	99.60	99.19	100.00	100.00
2	100.00	100.00	100.00	100.00	100.00	100.00
3	98.19	100.00	99.13	99.15	100.00	97.38
4	96.76	99.03	88.89	100.00	100.00	100.00
5	76.15	98.81	84.62	85.49	96.65	100.00
6	74.48	98.73	99.19	98.39	97.91	99.58
7	95.63	100.00	97.05	100.00	100.00	100.00
8	98.84	100.00	100.00	99.45	100.00	99.94
9	85.21	100.00	98.97	96.56	100.00	100.00
10	89.91	100.00	94.74	100.00	100.00	100.00
11	95.27	100.00	100.00	100.00	100.00	100.00
12	92.05	100.00	100.00	100.00	98.01	100.00
13	88.24	100.00	100.00	100.00	100.00	100.00
14	100.00	100.00	97.14	95.38	100.00	100.00
OA/%	90.91	99.62	97.02	98.00	99.43	99.79
AA/%	90.14	99.67	97.10	98.12	99.47	99.81
Kappa/%	92.20	99.59	96.76	97.83	99.39	99.77

Table 8. Comparison with other methods in the classification accuracy difference on Botswana dataset
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Table 8. Comparison with other methods in the classification accuracy difference on Botswana dataset
Class SVM DBMA ARNN SSAN 3DOC-SSAN
OA/% +8.88 +0.17 +2.77 +1.79 +0.36
AA/% +9.67 +0.14 +2.71 +1.69 +0.34
Kappa/% +7.57 +0.18 +3.01 +1.94 +0.38

Table 9. Comparison of the running time of the two methods
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Table 9. Comparison of the running time of the two methods
Class 3D Octave Bi-RNN 3D-OCM SSAM+SSICM
3DOC-RNN/s 26.212 16.062 — —
3DOC-SSAN/s — — 26.635 0.766 9

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Lianhui LIANG, Jun LI, Shaoquan ZHANG. Hyperspectral Images Classification Method Based on 3D Octave Convolution and Bi-RNN Attention Network[J]. Acta Photonica Sinica, 2021, 50(9): 0910001

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Paper Information

Category: Image Processing

Received: Jan. 4, 2021

Accepted: May. 6, 2021

Published Online: Oct. 22, 2021

The Author Email: ZHANG Shaoquan (zhangshaoquan1@163.com)

DOI:10.3788/gzxb20215009.0910001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Number of training and testing samples of the Pavia University dataset

Table 1. Number of training and testing samples of the Pavia University dataset

Table 2. Number of training and testing samples of the Botswana dataset

Table 2. Number of training and testing samples of the Botswana dataset

Table 3. Classification accuracy of different spatial size

Table 3. Classification accuracy of different spatial size

Table 4. Classification accuracy of different dropout

Table 4. Classification accuracy of different dropout

Table 5. Classification performance of different methods on Pavia University dataset

Table 5. Classification performance of different methods on Pavia University dataset

Table 6. Comparison with other methods in the classification accuracy difference on Botswana dataset

Table 6. Comparison with other methods in the classification accuracy difference on Botswana dataset

Table 7. Classification performance of different methods

Table 7. Classification performance of different methods

Table 8. Comparison with other methods in the classification accuracy difference on Botswana dataset

Table 8. Comparison with other methods in the classification accuracy difference on Botswana dataset

Table 9. Comparison of the running time of the two methods

Table 9. Comparison of the running time of the two methods