Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images

Li Wang; Wei Wang; Boni Liu

doi:10.3788/LOP57.141032

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 14 >Page 141032 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 14, 141032 (2020)

Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images

Li Wang^*, Wei Wang^**, and Boni Liu^***

Author Affiliations

Department of Electronic Engineering, Xi'an Aeronautical University, Xi'an, Shaanxi 710077, China

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DOI: 10.3788/LOP57.141032 Cite this Article Set citation alerts

Li Wang, Wei Wang, Boni Liu. Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141032 Copy Citation Text

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Fig. 1. Comparison between proposed HA_OMP and OMP

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Fig. 2. Original images of the 50th band. (a) Cuprite1; (b) Cuprite2; (c) Indian Pines; (d) Pavia University

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Fig. 3. Influence of evolution generation, population size, and atom number on HA_OMP. (a) Atom number is 50; (b) population size is 10; (c) evolution generation is 5

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Fig. 4. Reconstructed PSNR of Cuprite1 vs atom number. (a) OMP; (b) HA_OMP

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Fig. 5. Comparison between two reconstructed Cuprite2 images. (a) Reconstructed image of OMP algorithm; (b) reconstructed image of HA_OMP algorithm

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Fig. 6. Comparison between two reconstructed Pavia University images. (a) Reconstructed image of OMP algorithm; (b) reconstructed image of HA_OMP algorithm

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Scene	Originalbands	Originalimage size	Availablebands	Croppedimage size
Cuprite1	224	614×512	188	256×256
Cuprite2	224	614×512	188	256×256
Indian Pines	220	145×145	200	128×128
Pavia University	115	610×340	103	256×256

Table 1. Basic situation of four datasets

Scene	Algorithm	S_R=0.1		S_R=0.2		S_R=0.3		S_R=0.4		S_R=0.5
Scene	Algorithm	PSNR /dB	Atomnumber	PSNR /dB	Atomnumber	PSNR /dB	Atomnumber	PSNR /dB	Atomnumber	PSNR /dB	Atomnumber
Cuprite1	OMP	24.12	3	29.67	6	32.37	9	34.73	14	36.18	20
Cuprite1	HA_OMP	24.76	5	28.76	6	31.90	21	34.75	33	36.76	50
Cuprite2	OMP	21.88	3	27.22	5	29.12	8	31.04	14	32.05	18
Cuprite2	HA_OMP	23.04	4	26.79	6	28.70	19	30.12	34	32.79	46
Indian Pines	OMP	13.01	3	16.75	3	17.85	6	19.09	11	20.34	18
Indian Pines	HA_OMP	14.85	5	16.91	6	17.56	12	19.70	25	21.02	34
PaviaUniversity	OMP	19.78	2	22.03	4	23.54	7	25.38	14	27.02	22
PaviaUniversity	HA_OMP	20.24	4	22.57	7	24.05	14	25.79	27	27.26	43

Table 2. Optimal PSNR for single band and required atom number

Scene	Algorithm	Time	S_R=0.1	S_R=0.2	S_R=0.3	S_R=0.4	S_R=0.5
Cuprite1	OMP	Matching	1031.98	1054.99	1049.73	1063.31	1086.11
	OMP	Updating	9.65	11.36	12.13	11.57	12.50
	HA_OMP	Matching	40.02	41.79	43.59	45.26	47.52
	HA_OMP	Updating	1.90	2.12	2.20	2.19	2.13
Cuprite2	OMP	Matching	1056.40	1062.29	1079.52	1060.32	1090.40
	OMP	Updating	11.30	12.72	12.11	14.66	12.65
	HA_OMP	Matching	39.62	42.35	44.08	45.27	46.51
	HA_OMP	Updating	1.66	1.88	2.15	2.25	2.28

Table 3. Matching and updating time required for reconstructing single bands

Scene	Algorithm	S_R=0.1	S_R=0.2	S_R=0.3	S_R=0.4	S_R=0.5
Cuprite1	OMP	24.50	29.69	32.63	35.05	36.43
Cuprite1	HA_OMP	24.54	28.96	33.06	34.70	37.19
Cuprite2	OMP	21.96	27.49	29.42	31.34	32.52
Cuprite2	HA_OMP	22.68	27.33	29.30	32.09	33.15
Indian Pines	OMP	14.94	18.86	20.27	21.79	22.93
Indian Pines	HA_OMP	17.09	19.21	21.51	21.90	23.53
Pavia University	OMP	19.22	21.85	23.36	24.99	26.48
Pavia University	HA_OMP	20.64	22.38	23.74	25.43	26.59

Table 4. Average optimal PSNR of two algorithms under different sampling ratesdB

Scene	Algorithm	S_R=0.1	S_R=0.2	S_R=0.3	S_R=0.4	S_R=0.5
Cuprite1	OMP	1	1	1	1	1
Cuprite1	HA_OMP	15.10	8.62	12.39	12.71	11.34
Cuprite2	OMP	1	1	1	1	1
Cuprite2	HA_OMP	15.71	9.59	9.80	10.88	10.42
Indian Pines	OMP	1	1	1	1	1
Indian Pines	HA_OMP	17.67	14.11	9.58	11.82	11.19
Pavia University	OMP	1	1	1	1	1
Pavia University	HA_OMP	15.53	13.99	10.80	10.63	12.18

Table 5. Acceleration times of HA_OMP relative to OMP under different sampling rates

Li Wang, Wei Wang, Boni Liu. Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141032

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