Remote Sensing Technology and Application

Fusion of Multiscale Low-rank Representation and Two Way Recursive Filtering for Hyperspectral Image Classification

Mei LU, Jiatian LI, Wen LI, Mihong HU, Jiaxin YANG

Remote Sensing Technology and Application, 2024, 39(2): 393-404. DOI: 10.11873/j.issn.1004-0323.2024.2.0393

Class	Test	Train	SVM	PCA	IFRF	HiFi	CCJSR	R-VCANet	SSRN	MSLRR	MSLRR_ TWRF
Weeds_1	10	1 999	97.60	97.18	97.03	99.45	99.95	99.87	87.18	100	100
Weeds_2	10	3 716	98.93	98.18	99.98	99.46	99.47	99.64	99.82	99.68	100
fallow	10	1 966	88.34	90.35	99.81	98.11	93.51	97.22	95.06	89.35	100
fallow_P	10	1 384	98.31	98.57	91.47	98.92	99.17	99.55	98.73	98.66	98.36
Fallow_s	10	2 668	95.96	97.01	99.63	97.75	93.04	99.57	96.72	96.63	98.34
stubble	10	3 949	99.82	99.88	99.92	97.68	93.53	99.80	99.88	99.73	99.95
Celery	10	3 569	95.73	93.21	98.89	98.83	98.86	98.94	99.92	99.59	99.92
Grapes	10	11 261	71.62	68.56	97.48	66.37	76.58	70.89	82.71	80.99	98.43
Soil	10	6 193	99.47	97.74	99.99	99.76	98.52	98.86	99.32	98.02	100
Corn	10	3 268	79.98	86.71	99.66	84.39	95.65	86.16	96.19	88.28	98.27
Lettuce_4	10	1 058	83.00	87.09	98.14	94.04	91.00	95.43	95.12	96.77	100
Lettuce_5	10	1 917	91.84	86.98	97.65	99.93	95.62	99.95	97.22	95.33	98.59
Lettuce_6	10	906	88.75	88.49	92.71	99.44	77.55	98.79	97.31	98.13	97.99
Lettuce_7	10	1 060	90.98	95.75	91.65	95.14	94.15	95.25	98.67	96.07	98.51
Vinyard_U	10	7 258	49.80	49.12	79.70	76.84	53.42	77.62	75.55	62.25	99.37
Vinyard_T	10	1 797	97.20	92.22	99.96	92.75	99.57	91.93	99.88	97.39	99.91
OA	/	/	82.71±0.02	82.16±0.02	95.07±0.01	87.92±0.03	84.94±0.02	89.23±0.01	95.07±0.03	88.94±0.01	99.23±0.01
AA	/	/	89.21±0.01	89.19±0.01	96.48±0.01	93.68±0.01	91.22±0.1	94.34±0.01	94.96±0.03	93.55±0.1	99.23±0.08
Kappa	/	/	80.85±0.02	80.20±0.02	94.52±0.02	86.61±0.03	83.29±0.02	88.04±0.01	89.50±0.04	87.66±0.01	99.15±0.04

Table 4 Classification accuracy of different methods for Salinas

Other figure/table from this article