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

方法	SVM	PCA	IFRF	HiFi	CCJSR
Indian Pines	4.96	3.59	3.63	75.44	64.94
PaviaU	4.20	1.53	7.75	44.37	155.75
Salinas	11.43	7.03	6.60	48.08	357.59
方法	R-VCANet	SSRN	MSLRR	MSLRR_TWRF
Indian Pines	4 785.82	947.78	310.32	330.06
PaviaU	16 570.74	194.92	2 142.00	2 153.36
Salinas	18 093.95	959.88	1 153.38	1 170.84

Table 8 Running time of different compared methods

Other figure/table from this article

Fig.1 The flowchart to MSLRR_TWRF
Table 1 Dataset Description
Fig.2 Influence of parameter C on classification accuracy of MSLRR_TWRF method
Fig.3 Influence of parameter S_f on classification accuracy of MSLRR_TWRF method
Fig.4 Influence of parameter δ_r on classification accuracy of MSLRR_TWRF method
Table 2 Classification accuracy of different methods for Indian Pines
Table 3 Classification accuracy of different methods for PaviaU
Table 4 Classification accuracy of different methods for Salinas
Fig.5 Indian Pines image classification results obtained by different methods
Fig.6 PaviaU image classification results obtained by different methods
Fig.7 Salinas image classification results obtained by different methods
Table 5 Classification accuracy of different training samples for Indian Pines
Table 6 Classification accuracy of different training samples for PaviaU
Table 7 Classification accuracy of different training samples for Salinas