Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation

Xiangyang KONG; Jiao ZHANG; Hui WANG; Baogen XU

doi:10.3969/j.issn.1009-8518.2024.05.007

Journals >Spacecraft Recovery & Remote Sensing >Volume 45 >Issue 5 >Page 64 > Article

Spacecraft Recovery & Remote Sensing
Vol. 45, Issue 5, 64 (2024)

Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation

Xiangyang KONG¹, Jiao ZHANG², Hui WANG¹, and Baogen XU³

Author Affiliations

¹School of Education, Sichuan Polytechnic University, Deyang 618000, China

²No.1 Gas Production Plant of Southwest Oil and Gas Branch of Sinopec, Deyang 618000, China

³School of Science, East China Jiaotong University, Nanchang 330013, China

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DOI: 10.3969/j.issn.1009-8518.2024.05.007 Cite this Article

Xiangyang KONG, Jiao ZHANG, Hui WANG, Baogen XU. Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(5): 64 Copy Citation Text

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控制指标		评价指标	含噪	LRMID	DL0	RBSR	LRTD	NLLRTDTV
I=60	r=0.15	MPSNR	16.35	33.27	40.61	42.35	41.29	44.63
		MSSIM	0.5533	0.8828	0.9264	0.9812	0.9772	0.9906
		MSAM	0.5971	0.4122	0.2025	0.1936	0.1975	0.1129
		MPSNR	9.11	28.24	37.62	39.19	38.68	41.95
	r=0.65	MSSIM	0.3401	0.8664	0.8953	0.9543	0.9589	0.9788
		MSAM	0.7358	0.4563	0.2235	0.2087	0.2116	0.1421
		MPSNR	7.56	27.32	35.64	37.88	36.03	38.69
	r=0.95	MSSIM	0.1303	0.7568	0.8459	0.9108	0.8572	0.9665
		MSAM	0.8716	0.4868	0.2481	0.2197	0.2386	0.1608
I=90	r=0.15	MPSNR	12.83	32.65	39.37	41.58	40.39	43.27
		MSSIM	0.4097	0.8711	0.9164	0.9775	0.9682	0.9889
		MSAM	0.6296	0.4718	0.2461	0.2239	0.2364	0.1558
	r=0.65	MPSNR	5.59	26.31	32.54	36.85	33.13	39.52
		MSSIM	0.1441	0.8367	0.8755	0.9248	0.9169	0.9582
		MSAM	0.8018	0.4776	0.2451	0.2168	0.2318	0.1582
	r=0.95	MPSNR	4.04	23.55	29.17	31.76	30.18	34.69
		MSSIM	0.0852	0.7338	0.8075	0.8913	0.8367	0.9384
		MSAM	0.9177	0.5013	0.2639	0.2267	0.2574	0.1772

Table 1. MPSNR, MSSIM and MSAM values for random length stripes

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条带类型	控制指标		评价指标	含噪	LRMID	DL0	RBSR	LRTD	NLLRTDTV
周期			MPSNR	22.56	42.36	51.62	51.89	52.04	54.84
		r=0.15	MSSIM	0.594 9	0.999 2	0.999 6	0.999 3	0.999 7	0.999 9
			MSAM	0.091 3	0.020 5	0.021 3	0.022 1	0.015 8	0.011 6
			MPSNR	14.78	41.02	48.97	50.85	51.62	53.18
	I=60	r=0.65	MSSIM	0.212 8	0.979 6	0.988 4	0.990 2	0.994 2	0.997 9
			MSAM	0.319 9	0.110 3	0.095 1	0.096 2	0.090 4	0.083 6
			MPSNR	13.02	38.91	49.02	49.11	43.54	53.12
		r=0.95	MSSIM	0.153 9	0.867 4	0.923 8	0.938 1	0.906 4	0.971 5
			MSAM	0.517 4	0.201 5	0.164 2	0.153 6	0.163 8	0.101 4
				MPSNR	19.04	42.13	51.21	51.66	51.95	54.28
			r=0.15	MSSIM	0.469 6	0.998 7	0.999 1	0.999 4	0.999 5	0.999 7
				MSAM	0.133 1	0.030 2	0.028 7	0.024 8	0.016 9	0.012 2
				MPSNR	11.26	37.61	41.64	40.95	41.06	43.28
		I=90	r=0.65	MSSIM	0.121 2	0.856 7	0.918 8	0.935 4	0.944 8	0.976 4
				MSAM	0.449 9	0.200 3	0.155 2	0.146 8	0.111 2	0.106 8
				MPSNR	9.51	33.58	40.91	40.28	38.11	43.02
			r=0.95	MSSIM	0.077 9	0.648 8	0.926 5	0.921 9	0.801 7	0.973 3
				MSAM	0.728 4	0.223 6	0.166 5	0.158 9	0.185 2	0.110 1
非周期			MPSNR	20.82	40.33	49.24	48.61	50.72	53.25
		r=0.15	MSSIM	0.556 6	0.998 4	0.999 1	0.998 9	0.999 2	0.999 5
			MSAM	0.290 9	0.096 7	0.095 1	0.095 5	0.086 5	0.031 1
	I=60		MPSNR	14.44	39.95	46.86	49.37	49.82	52.19
		r=0.65	MSSIM	0.210 1	0.959 8	0.978 5	0.986 6	0.987 1	0.991 3
			MSAM	0.594 2	0.216 9	0.171 9	0.138 2	0.126 4	0.101 7
			MPSNR	12.79	38.39	47.16	47.81	41.95	52.64
		r=0.95	MSSIM	0.153 7	0.866 9	0.924 1	0.939 2	0.903 5	0.970 1
			MSAM	0.679 6	0.211 8	0.176 3	0.160 8	0.173 7	0.115 6
			MPSNR	17.31	40.67	50.68	50.94	51.37	53.84
		r=0.15	MSSIM	0.450 2	0.985 7	0.986 4	0.987 2	0.988 7	0.992 8
			MSAM	0.399 8	0.102 8	0.101 4	0.103 5	0.098 5	0.087 2
			MPSNR	10.92	37.62	43.24	44.84	45.37	47.91
	I=90	r=0.65	MSSIM	0.113 4	0.840 8	0.902 5	0.911 6	0.933 1	0.952 5
			MSAM	0.771 4	0.238 1	0.182 5	0.159 8	0.134 9	0.118 2
			MPSNR	9.26	33.12	38.85	38.11	37.54	43.86
		r=0.95	MSSIM	0.072 0	0.833 6	0.905 3	0.899 4	0.798 7	0.964 8
			MSAM	0.863 2	0.259 1	0.168 4	0.167 2	0.192 9	0.120 1

Table 2. MPSNR, MSSIM and MSAM values in the case of global stripes

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数据集	指标	LRMID	DL0	RBSR	LRTD	NLLRTDTV
“高分五号”	MICV	76.81	84.68	71.94	87.42	95.68
“高分五号”	MMRD	0.1136	0.0961	0.0983	0.0867	0.0802
EO-1 Hyperion	MICV	62.81	63.17	65.79	64.59	71.35
EO-1 Hyperion	MMRD	0.0524	0.0487	0.0469	0.0457	0.0403

Table 3. Comparison of MICV and MMRD values with no-reference evaluation index with real image

Xiangyang KONG, Jiao ZHANG, Hui WANG, Baogen XU. Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(5): 64

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