Fig 1.
Part of pavia centre hyperspectral image.
(a) The HSI in false color (RGB 3, 65,101), (b) Ground truth.
Fig 2.
The paviaU hyperspectral remote sensing image.
(a) The HSI in false color (RGB 64, 101,1), (b) Ground truth.
Fig 3.
Analysis of parameter k (a) Change in the AC with various values of k. (b) Change in the NMI with various values of k.
Fig 4.
Cluster maps of the different methods with the PaviaU image(a)KASP-ASSC(k = 10) (b)RASP-ASSC(k = 10) (c)LI-ASSC(k = 10) (d)LI-ASP(k = 10) (e)LLE-ASSC(k = 20) (f)True Ground.
Table 1.
Quantitative evaluation of the different clustering algorithms for paviaU image.
Fig 5.
Cluster maps of the different methods with the Pavia centre image(a)KASP-ASSC(k = 20) (b) RASP-ASSC(k = 20) (c) LI-ASSC(k = 20) (d) LI-ASP(k = 10) (e) LLE-ASSC(k = 15) (f)True Ground.
Table 2.
Quantitative evaluation of the different clustering algorithms for pavia centre image.
Table 3.
The AC (std) and NMI (std) of clustering results on pavia centre image.
Table 4.
Z values in the McNemar’s test result on paviaU image.
And the 5% level of significance is selected.
Table 5.
Z values in the McNemar’s test result on pavia centre image.
And the 5% level of significance is selected.