Infrared and Laser Engineering, Volume. 53, Issue 10, 20240215(2024)
BYOL-based self-supervised learning for hyperspectral image classification
Figures & Tables(17)
Fig. 3. SSTN algorithm architecture. (a)
Fig. 4. Directional region generation in vertical direction. (a) Area scanned from top to bottom; (b) Area scanned from bottom to top; (c) Merged area in two directions; (d) Scanning performance example
Fig. 5. Superpixel clustering result map of Indian Pines dataset. (a) Original image； (b) Edge image； (c) Superpixel clustering image
Fig. 6. Indian Pines dataset. (a) Pseudo-color image; (b) Corresponding ground object type; (c) Number of sample sets
Fig. 7. University of Pavia dataset. (a) Pseudo-color image; (b) Corresponding ground object type; (c) Number of sample sets
Fig. 8. Salinas dataset. (a) Pseudo-color image; (b) Corresponding ground object type; (c) Number of sample sets
Fig. 9. Classification maps of different methods on Indian Pines dataset
Fig. 10. Classification maps of different methods on University of Pavia dataset
Fig. 11. Classification maps of different methods on Salinas dataset
Fig. 12. The impact of different ratios of pretraining samples on overall accuracy (OA)
Fig. 13. The effect of different number of superpixel blocks on OA on the Indian Pines dataset
Table 1. Classification results of different methods on Indian Pines dataset
View tableView in ArticleTable 1. Classification results of different methods on Indian Pines dataset
Class SuperPCA S3PCA ContrastNet SSCL N2SSL BSSL 1 93.18±0.38 100.00±0.00 82.54±3.16 85.17±2.46 92.56±1.32 93.09±0.69 2 89.66±2.65 85.94±3.16 81.45±2.60 93.75±1.57 94.34±4.47 89.36±2.44 3 90.75±2.23 92.65±5.64 89.45±2.31 90.38±1.66 87.52±7.45 94.27±0.83 4 97.20±2.06 95.41±2.30 84.34±2.15 88.84±2.44 94.28±6.25 81.66±2.30 5 96.03±2.48 91.22±2.45 82.97±1.45 87.91±1.78 98.16±2.43 91.77±0.95 6 94.13±4.45 93.19±3.22 93.56±1.56 92.09±1.38 98.75±1.26 98.09±0.65 7 92.34±3.36 91.00±1.83 92.67±1.43 91.83±1.49 96.38±2.61 98.22±0.65 8 98.24±2.62 95.65±3.55 96.54±0.85 97.89±0.45 100.00±0.00 98.93±0.41 9 91.03±4.43 96.20±3.10 99.65±0.93 96.94±0.64 65.25±1.83 97.89±0.97 10 89.15±4.35 91.10±4.50 87.54±2.68 85.97±1.55 89.93±8.40 94.87±0.85 11 94.78±1.44 96.68±1.02 87.30±2.32 92.85±2.10 94.53±2.75 97.52±0.78 12 93.53±1.57 95.01±1.54 93.79±1.46 96.09±0.89 96.28±0.93 97.96±1.30 13 98.02±1.72 99.43±0.05 98.56±0.91 95.97±0.91 99.57±2.15 97.06±0.80 14 99.86±0.03 99.86±0.03 96.65±0.47 98.98±0.47 99.07±0.48 98.08±0.47 15 98.37±0.28 97.31±1.86 85.56±2.53 95.12±1.20 96.46±5.36 97.29±0.89 16 97.60±1.45 98.41±1.00 92.57±1.46 96.96±1.17 99.18±1.14 95.54±0.89 OA (94.53±0.68)% (94.80±1.22)% (90.17±0.98)% (92.73±1.07)% (94.58±1.96)% (95.85±0.69)% AA (94.62±0.74)% (94.94±0.64)% (90.32±1.17)% (92.92±0.88)% (93.89±2.25)% (95.10±0.63)% Kappa×100 93.73±0.78 94.96±0.74 89.36±1.21 93.12±0.83 94.27±1.67 95.02±0.76 recall 94.66±0.32 94.52±0.83 90.85±0.37 93.41 ± 1.5894.78±2.74 95.75±1.54 f1-score 94.65±0.74 94.02±0.55 90.38±0.89 92.45 ± 1.3595.38±1.85 95.67±2.85
Table 2. Classification results of different methods on University of Pavia dataset
View tableView in ArticleTable 2. Classification results of different methods on University of Pavia dataset
Class SuperPCA S3PCA ContrastNet SSCL N2SSL BSSL 1 79.65±3.14 89.53±4.10 94.72±1.44 93.04±1.60 95.24±1.74 97.32±0.89 2 94.09±1.97 93.72±2.82 95.13±1.57 95.85±1.36 97.61±1.19 98.74±0.47 3 97.54±0.34 99.66±0.10 89.05±3.55 93.66±2.35 95.67±2.47 92.08±1.28 4 86.60±2.33 91.73±2.31 91.56±1.56 92.08±2.09 97.71±0.89 92.23±1.14 5 96.43±2.08 99.60±0.26 94.78±1.44 98.37±0.95 99.08±0.46 99.55±0.09 6 94.04±2.17 98.88±0.73 96.58±0.89 95.53±1.60 96.83±1.33 97.21±0.35 7 94.01±1.87 98.39±0.86 96.49±0.84 97.59±0.73 95.28±2.23 95.16±0.71 8 92.08±3.16 97.36±0.91 95.97±1.00 93.60±1.10 92.96±3.91 98.34±0.43 9 98.69±1.62 93.76±3.60 93.27±1.88 95.81±1.47 94.63±2.33 95.02±1.08 OA (91.47±0.65)% (96.09±1.28)% (95.39±1.17)% (95.62±0.90)% (95.29±2.45)% (95.93±0.61)% AA (92.57±0.45)% (95.85±0.81)% (94.17±1.33)% (95.06±0.67)% (96.11±1.84)% (96.18±0.63)% Kappa×100 88.80±0.82 94.86±1.66 95.7±1.45 95.41±0.64 95.34±2.31 95.91±0.47 recall 92.08±1.25 95.49±1.28 94.56±1.28 95.27±0.58 95.36±0.67 95.68±0.81 f1-score 91.20±0.84 96.26±0.56 94.82±1.92 95.34±0.72 95.44±1.64 95.82±1.26
Table 3. Classification results of different methods on Salinas dataset
View tableView in ArticleTable 3. Classification results of different methods on Salinas dataset
Class SuperPCA S3PCA ContrastNet SSCL N2SSL BSSL 1 100.00±0.00 100.00±0.00 100.00±0.00 100.00±0.00 100.00±0.00 100.00±0.00 2 99.82±0.11 99.84±0.14 99.21±0.58 98.18±0.50 99.57±0.85 98.68±0.29 3 96.94±2.66 97.44±1.59 97.46±0.93 100.00±0.00 97.41±0.42 100.00±0.00 4 98.97±0.36 98.91±0.49 98.25±0.85 98.48±0.55 98.64±0.67 99.64±0.19 5 99.46±0.04 98.99±0.20 98.45±0.55 98.95±0.90 99.48±0.51 97.74±0.93 6 99.59±0.07 99.90±0.01 99.34±0.07 100.00±0.00 100.00±0.00 100.00±0.00 7 98.95±1.20 98.82±1.22 96.54±0.25 98.58±0.73 100.00±0.00 99.05±0.43 8 99.36±0.18 99.88±0.23 97.98±1.05 96.35±1.35 95.59±2.41 99.79±0.16 9 99.66±0.27 99.09±1.49 98.15±1.16 100.00±0.00 100.00±0.00 98.86±0.83 10 97.28±0.98 95.81±2.66 94.12±1.66 96.08±0.75 99.34±0.84 99.02±0.31 11 98.17±1.30 98.54±1.20 93.79±1.68 96.07±0.53 94.84±1.65 99.39±0.45 12 99.79±0.22 99.95±0.10 98.38±1.46 98.23±0.52 99.56±0.28 99.95±0.28 13 98.19±0.00 98.19±0.00 96.87±1.54 95.33±0.87 98.84±0.64 100.00±0.00 14 97.65±1.00 98.04±0.91 95.88±1.47 97.59±0.81 99.57±0.48 100.00±0.00 15 99.43±0.36 99.56±0.36 99.65±0.08 100.00±0.00 92.86±0.85 99.97±0.00 16 99.23±0.65 98.92±0.52 97.11±1.46 100.00±0.00 99.28±0.34 100.00±0.00 OA (99.19±0.16)% (99.16±0.49)% (97.61±0.78)% (98.57±0.68)% (98.97±0.71)% (99.52±0.27)% AA (98.91±0.27)% (98.87±0.49)% (97.57±0.52)% (98.37±0.43)% (98.44±0.83)% (99.51±0.23)% Kappa×100 99.09±0.17 99.06±0.55 97.13±0.46 98.06±0.33 97.84±0.58 99.44±0.12 recall 99.37±0.82 99.14±0.36 97.28±0.68 98.37±1.07 98.37±0.63 99.27±0.34 f1-score 98.85±0.67 98.68±0.77 97.61±1.85 98.75±0.67 97.86±1.22 99.16±0.85
Table 4. Ablation trial results on three datasets
View tableView in ArticleTable 4. Ablation trial results on three datasets
Datasets Evaluation indicators BSSL-noSP BSSL Indian Pines OA (92.13±0.28)% (95.85±0.69)% AA (91.42±0.83)% (95.10±0.63)% Kappa 91.97±0.66 95.02±0.76 University of Pavia OA (92.71±0.38)% (95.93±0.61)% AA (92.39±0.74)% (96.18±0.63)% Kappa 92.06±0.86 95.91±0.47 Salinas OA (94.79±0.56)% (99.52±0.27)% AA (94.48±0.73)% (99.51±0.23)% Kappa 94.27±0.22 99.44±0.12
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Xizhen HAN, Zhengang JIANG, Yuanyuan LIU, Jian ZHAO, Qiang SUN, Jianzhuo LIU. BYOL-based self-supervised learning for hyperspectral image classification[J]. Infrared and Laser Engineering, 2024, 53(10): 20240215
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Category: 光谱学
Received: Jun. 10, 2024
Accepted: --
Published Online: Dec. 13, 2024
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