Hyperspectral Semi-Supervised Classification Algorithm Based on Improved Ladder Network

Shihao Guan; Guang Yang; Shan Lu; Chunbai Jin; Hao Li; Zhaohong Xu

doi:10.3788/LOP202158.2428008

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 24 >Page 2428008 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 24, 2428008 (2021)

Hyperspectral Semi-Supervised Classification Algorithm Based on Improved Ladder Network

Shihao Guan¹, Guang Yang^1、*, Shan Lu², Chunbai Jin¹, Hao Li³, and Zhaohong Xu⁴

Author Affiliations

¹Aviation University of Air Force, Changchun, Jilin 130022, China

²School of Geographic Science, Northeast Normal University, Changchun, Jilin 130024, China

³95910 Troop of PLA, Jiuquan, Gansu 735000, China

⁴95795Troop of PLA, Guilin, Guangxi 541000, China

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DOI: 10.3788/LOP202158.2428008 Cite this Article Set citation alerts

Shihao Guan, Guang Yang, Shan Lu, Chunbai Jin, Hao Li, Zhaohong Xu. Hyperspectral Semi-Supervised Classification Algorithm Based on Improved Ladder Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428008 Copy Citation Text

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Fig. 1. Process of two-dimensional convolution

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Fig. 2. Process of three-dimensional convolution

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Fig. 3. Structure of LSTM network

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Fig. 4. Structure of ladder network

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Fig. 5. Structure of 3D-CNN-LSTM network

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Fig. 6. Model of semi-supervised classification algorithm based on improved ladder network

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Fig. 7. Structure of 3D-CNN-LSTM ladder network

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Fig. 8. True color image and ground truth map of Pavia University dataset. (a) True color image; (b) ground truth map

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Fig. 9. True color image and ground truth map of Indian Pines dataset. (a) True color image; (b) ground truth map

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Fig. 10. Classification results of different algorithms on Pavia University dataset. (a) True color image; (b) feature label map; (c) M3D-DCNN algorithm; (d) 3D-CNN-LSTM algorithm; (e) SS-CNN algorithm; (f) S4CNN algorithm; (g) 3D-CNN-LSTM-LN algorithm

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Fig. 11. Classification results of different algorithms on Indian Pines dataset. (a) True color image; (b) feature label map; (c) M3D-DCNN algorithm; (d) 3D-CNN-LSTM algorithm; (e) SS-CNN algorithm; (f) S4CNN algorithm; (g) 3D-CNN-LSTM-LN algorithm

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Layer	Kernel size	Input size	Output size	Number of convolution kernels	Activation function
Input	--	--	11×11×30	--	--
Conv-1(Conv 3D)	3×3×1	11×11×30	9×9×30	4	--
BN-1	--	9×9×30	9×9×30	--	ReLU
Pooling-1(MaxPooling)	1×1×2	9×9×30	9×9×15	--	--
Conv-2(Conv 3D)	3×3×1	9×9×15	7×7×15	8	--
BN-2	--	7×7×15	7×7×15	--	ReLU
Pooling-2(MaxPooling)	1×1×2	7×7×15	7×7×7	--	--
Conv-3(Conv 3D)	3×3×1	7×7×7	5×5×7	16	--
BN-3	--	5×5×7	5×5×7	--	ReLU
Pooling-3(MaxPooling)	1×1×2	5×5×7	5×5×3	--	--
Linear-1(flatten)	--	5×5×3	1×75	--	--
RNN-1(Bi-LSTM)	--	1200×1	400×1	--	Tanh

Table 1. Network structure of encoder in 3D-CNN-LSTM-LN model

No.	Class	M3D-DCNN	3D-CNN-LSTM	SS-CNN	S4CNN	3D-CNN-LSTM-LN
1	Asphalt	93.25	93.03	94.98	96.94	99.21
2	Meadow	98.14	96.99	96.84	99.11	99.63
3	Gravel	76.83	75.13	89.16	86.97	96.22
4	Tree	97.78	91.45	93.50	98.33	98.94
5	Metal sheet	100.00	96.63	99.33	99.78	99.66
6	Bare soil	92.87	90.31	92.28	97.40	99.18
7	Bitumen	80.56	86.97	83.21	91.76	97.79
8	Brick	83.14	83.95	90.92	93.50	98.13
9	Shadow	94.25	88.52	96.84	97.00	98.55
OA	--	93.73	92.46	94.46	97.06	99.03
AA	--	90.62	89.13	92.96	95.64	98.59
Kappa	--	91.70	89.90	92.80	96.20	98.80

Table 2. Classification accuracy of different algorithms on Pavia University dataset unit: %

No.	Class	M3D-DCNN	3D-CNN-LSTM	SS-CNN	S4CNN	3D-CNN-LSTM-LN
1	Alfalfa	39.12	77.65	81.91	97.23	96.85
2	Corn-notill	77.95	82.45	83.24	91.77	96.93
3	Corn-mintill	88.67	82.36	74.34	89.34	93.97
4	Corn	87.02	66.75	79.67	96.38	97.15
5	Grass-pasture	95.43	93.17	97.65	92.93	95.49
6	Grass-tree	98.78	95.36	88.96	99.36	98.17
7	Grass-pasture-mowed	70.31	88.95	75.02	97.72	62.13
8	Hay-windrowed	95.33	97.34	89.61	98.33	98.88
9	Oat	53.35	56.02	82.47	72.03	57.15
10	Soybean-notill	81.63	90.14	82.35	92.19	97.31
11	Soybean-mintill	86.27	88.46	82.63	92.84	98.19
12	Soybean-clean	80.68	74.28	83.28	83.96	92.56
13	Wheat	99.45	97.67	81.36	97.95	99.70
14	Wood	94.59	98.32	87.64	97.71	99.65
15	Building-grass-tree-drive	76.95	90.24	80.27	89.72	95.12
16	Stone-steel-tower	95.27	46.83	97.79	90.98	93.35
OA	--	87.20	88.83	89.56	93.34	97.04
AA	--	82.51	83.22	84.26	82.40	92.03
Kappa	--	85.30	87.20	88.80	92.30	96.60

Table 3. Classification accuracy of different algorithms on Indian Pines dataset unit: %

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