Table 3 Classification performance of different methodologies on the UP dataset.

From: Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification

Class

SVM

LPP_LBP_BLS

SSSKRR

MSF-PCs

SSFTT

CVSSN

AMS-M2ESL

MASSFormer

CiSSA_MLTP

1

\(90.59\pm 1.93\)

\(83.85\pm 2.20\)

\(94.40\pm 1.65\)

\({\varvec {98.83}}\pm {\varvec {0.14}}\)

\(95.20\pm 0.77\)

\(92.54\pm 2.17\)

\(96.00\pm 0.56\)

\(97.16\pm 0.77\)

\(96.70\pm 0.46\)

2

\(92.56\pm 0.56\)

\(97.94\pm 0.62\)

\(99.73\pm 0.13\)

\({\varvec {100.0}}\pm {\varvec {0.00}}\)

\(97.79\pm 0.58\)

\(98.41\pm 0.38\)

\(99.32\pm 0.27\)

\(99.21\pm 0.36\)

\(99.60\pm 0.14\)

3

\(68.01\pm 2.53\)

\(88.76\pm 4.69\)

\(83.68\pm 3.82\)

\(96.99\pm 0.18\)

\(89.86\pm 2.47\)

\(84.19\pm 4.31\)

\(96.17\pm 0.29\)

\(94.63\pm 1.37\)

\({\varvec {97.99}}\pm {\varvec {0.26}}\)

4

\(92.87\pm 4.37\)

\(56.41\pm 4.36\)

\(87.62\pm 1.94\)

\(93.65\pm 0.35\)

\(99.05\pm 0.54\)

\(94.57\pm 0.70\)

\({\varvec {99.77}}\pm {\varvec {0.03}}\)

\(97.23\pm 0.67\)

\(94.42\pm 1.19\)

5

\(99.53\pm 0.24\)

\(91.60\pm 5.47\)

\(83.11\pm 3.08\)

\({\varvec {100.0}}\pm {\varvec {0.00}}\)

\(99.97\pm 0.04\)

\(98.36\pm 1.28\)

\(99.89\pm 0.03\)

\(99.28\pm 0.43\)

\(95.91\pm 0.37\)

6

\(84.24\pm 1.93\)

\(99.91\pm 0.10\)

\(95.76\pm 0.79\)

\({\varvec {100.0}}\pm {\varvec {0.00}}\)

\(97.90\pm 0.89\)

\(93.36\pm 1.08\)

\(99.22\pm 0.10\)

\(99.82\pm 0.18\)

\(99.82\pm 0.10\)

7

\(69.25\pm 3.32\)

\(91.40\pm 7.38\)

\(93.58\pm 1.50\)

\(99.83\pm 0.03\)

\(96.55\pm 1.33\)

\(87.94\pm 3.97\)

\({\varvec {99.92}}\pm {\varvec {0.10}}\)

\(94.72\pm 1.85\)

\(98.09\pm 0.49\)

8

\(78.08\pm 2.62\)

\(90.71\pm 4.60\)

\(85.96\pm 4.86\)

\(97.65\pm 0.50\)

\(85.71\pm 1.86\)

\(91.84\pm 1.15\)

\(96.21\pm 0.28\)

\(93.84\pm 2.21\)

\({\varvec {98.82}}\pm {\varvec {0.26}}\)

9

\({\varvec {99.98}}\pm {\varvec {0.04}}\)

\(43.97\pm 4.56\)

\(68.34\pm 8.83\)

\(73.55\pm 0.70\)

\(97.64\pm 0.53\)

\(96.86\pm 3.07\)

\(99.72\pm 0.35\)

\(92.96\pm 2.10\)

\(99.33\pm 0.20\)

OA (%)

\(88.34\pm 0.53\)

\(90.34\pm 0.65\)

\(94.19\pm 0.49\)

\(98.42\pm 0.06\)

\(94.80\pm 0.40\)

\(94.97\pm 0.67\)

\(98.43\pm 0.19\)

\(97.17\pm 0.26\)

\({\varvec {98.49}}\pm {\varvec {0.04}}\)

AA (%)

\(86.12\pm 1.01\)

\(82.73\pm 1.35\)

\(88.02\pm 1.20\)

\(95.61\pm 0.13\)

\(95.52\pm 0.35\)

\(93.52\pm 0.61\)

\({\varvec {98.47}}\pm {\varvec {0.11}}\)

\(96.24\pm 0.41\)

\(97.85\pm 0.08\)

\(\kappa\) (%)

\(84.46\pm 0.66\)

\(87.16\pm 0.87\)

\(92.27\pm 0.65\)

\(97.91\pm 0.08\)

\(96.08\pm 0.32\)

\(93.33\pm 0.89\)

\(97.92\pm 0.26\)

\(97.87\pm 0.20\)

\({\varvec {98.00}}\pm {\varvec {0.05}}\)