Remote Sensing Technology and Application, Volume. 39, Issue 2, 393(2024)
Fusion of Multiscale Low-rank Representation and Two Way Recursive Filtering for Hyperspectral Image Classification
Figures & Tables(15)
Fig. 2. Influence of parameter C on classification accuracy of MSLRR_TWRF method
Fig. 3. Influence of parameter Sf on classification accuracy of MSLRR_TWRF method
Fig. 4. Influence of parameter δr on classification accuracy of MSLRR_TWRF method
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 1. Dataset Description
View tableView in ArticleTable 1. Dataset Description
数据集 光谱范围
/nm
空间分辨
率/m
图像尺寸 波段数 类别数 带标签样本 Indian Pines 400~2 500 20×20 145×145 200 16 10 249 PaviaU 430~860 1.3×1.3 610×340 103 9 42 776 Salinas 400~2 500 3.7×3.7 512×217 204 16 54 129
Table 2. Classification accuracy of different methods for Indian Pines
View tableView in ArticleTable 2. Classification accuracy of different methods for Indian Pines
Class Train Test SVM PCA IFRF HiFi CCJSR R-VCANet SSRN MSLRR MSLRR_
TWRF
Alfalfa 10 36 38.81 23.61 77.45 98.61 67.24 100 90 98.33 100 Corn_n 10 1 418 50.69 52.56 75.68 67.48 63.88 49.53 74.76 71.09 70.92 Corn_m 10 820 42.22 39.41 62.20 84.87 64.30 73.49 73.47 75.51 79.52 Corn 10 227 27.06 25.06 54.89 92.73 41.34 94.63 86.64 96.21 96.78 Grass_m 10 473 75.37 60.97 88.19 78.48 95.78 88.92 95.86 85.67 86.96 Grass_t 10 720 84.91 79.21 91.15 96.60 95.82 94.47 93.33 99.64 98.03 Grass_P 10 18 39.69 26.64 50.84 98.89 37.46 100 79.82 97.78 97.78 Hay_w 10 468 96.20 97.33 100 96.88 99.57 95.73 91.75 100 100 Oats 10 10 14.18 21.27 30.77 100 13.77 100 100 100 100 Soybean_n 10 962 52.26 39.96 69.67 85.30 62.36 76.17 77.54 84.83 86.75 Soybean_m 10 2 445 66.89 63.46 87.61 70.49 78.69 65.38 83.93 89.91 91.90 Soybean_c 10 583 33.15 33.63 77.36 84.15 68.03 71.90 79.80 87.32 91.56 Wheat 10 195 79.56 78.75 76.86 99.33 90.40 99.18 98.08 99.49 99.49 Woods 10 1 255 91.32 87.97 97.97 93.64 96.56 91.68 93.45 90.91 97.10 Buildings 10 376 38.23 35.28 78.70 89.65 73.62 81.49 83.29 91.65 92.69 stone 10 83 83.48 84.72 96.75 99.28 90.75 99.52 97.02 95.78 97.95 OA / / 59.44±0.03 54.60±0.03 79.14±0.03 81.97±0.02 74.93±0.02 75.49±0.01 80.63±0.04 87.04±0.03 89.05 ±0.02AA / / 57.13±0.03 53.11±0.02 76.01±0.05 89.77±0.01 71.22±0.2 86.38±0.01 87.42±0.02 91.51±0.09 92.96 ±0.08Kappa / / 54.47±0.03 49.27±0.04 76.51±0.04 79.69±0.03 71.76±0.02 72.44±0.03 77.97±0.05 85.20±0.04 87.48 ±0.03
Table 3. Classification accuracy of different methods for PaviaU
View tableView in ArticleTable 3. Classification accuracy of different methods for PaviaU
Class Train Test SVM PCA IFRF HiFi CCJSR R-VCANet SSRN MSLRR MSLRR_
TWRF
Asphalt 10 18 639 88.79 66.01 69.94 72.08 90.98 70.93 93.22 84.87 88.77 Meadows 10 2 089 84.09 65.53 94.43 79.36 84.62 76.75 95.44 92.16 95.44 Gravel 10 3 054 46.35 57.32 61.05 78.44 42.07 87.18 80.10 94.98 98.23 Trees 10 1 335 54.04 91.72 53.87 78.79 67.26 93.74 98.29 93.15 88.87 Sheets 10 5 019 85.41 99.75 97.48 89.62 66.77 99.97 98.69 97.69 99.57 Soil 10 1 320 36.31 56.21 77.53 77.30 35.68 85.62 94.77 84.36 98.74 Bitumen 10 3 672 42.66 88.58 66.62 92.73 60.25 93.38 90.82 96.11 99.96 Bricks 10 937 70.29 72.46 58.13 72.57 45.61 81.27 79.07 92.08 97.27 Shadows 10 180 98.85 99.64 49.96 99.01 75.25 98.34 99.83 99.45 99.66 OA / / 64.87±0.05 69.11±0.05 74.53±0.06 78.48±0.05 61.05±0.04 80.72±0.023 92.35±0.01 90.77±0.02 94.98 ±0.02AA / / 67.42±0.04 77.14±0.18 69.89±0.04 82.21±0.03 63.17±0.2 87.46±0.011 92.25±0.02 92.76±0.05 96.28 ±0.04Kappa / / 56.54±0.06 61.40±0.05 67.85±0.06 72.50±0.05 50.82±0.04 75.52±0.03 89.90±0.02 87.91±0.02 93.42 ±0.03
Table 4. Classification accuracy of different methods for Salinas
View tableView in ArticleTable 4. Classification accuracy of different methods for Salinas
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 ±0.01AA / / 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 ±0.08Kappa / / 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 5. Classification accuracy of different training samples for Indian Pines
View tableView in ArticleTable 5. Classification accuracy of different training samples for Indian Pines
方法 指标 训练样本数 10 20 30 40 50 SVM OA/% 59.44 68.60 73.25 75.97 78.56 AA/% 57.13 64.52 70.33 73.25 75.85 Kappa/% 54.47 64.56 69.71 72.75 75.60 PCA OA/% 54.60 62.37 67.67 70.25 72.27 AA/% 53.11 59.89 65.17 68.07 71.23 Kappa/% 49.27 57.73 63.51 66.33 68.53 IFRF OA/% 79.14 89.85 93.40 95.33 96.21 AA/% 76.01 85.70 92.35 93.82 95.59 Kappa/% 76.51 88.47 92.47 94.65 95.66 HiFi OA/% 81.97 88.90 91.56 93.02 94.36 AA/% 89.77 94.01 95.43 96.34 96.90 Kappa/% 79.69 87.41 90.38 92.02 93.54 CCJSR OA/% 74.93 81.57 86.03 88.17 89.45 AA/% 71.22 75.53 78.73 79.34 80.67 Kappa/% 71.76 79.09 84.15 86.23 87.95 R-VCANet OA/% 75.49 83.23 86.72 89.16 91.05 AA/% 86.38 91.33 94.01 95.08 95.92 Kappa/% 72.44 80.99 84.95 87.67 90.12 SSRN OA/% 80.63 89.87 94.92 95.34 97.37 AA/% 87.42 84.55 90.18 85.19 91.57 Kappav 77.97 88.45 94.19 94.70 97.41 MSLRR OA/% 87.04 92.75 94.42 94.71 95.08 AA/% 91.51 94.98 96.22 96.58 96.63 Kappa/% 85.20 91.70 93.60 93.93 94.35 MSLRR_TWRF OA/% 89.05 94.95 96.82 97.66 98.00 AA/% 92.96 96.71 97.93 98.40 98.59 Kappa/% 87.48 94.21 96.36 97.32 97.70
Table 6. Classification accuracy of different training samples for PaviaU
View tableView in ArticleTable 6. Classification accuracy of different training samples for PaviaU
方法 指标 训练样本数 10 20 30 40 50 SVM OA/% 64.87 75.92 78.44 82.11 83.19 AA/% 67.42 75.49 76.49 79.75 80.36 Kappa/% 56.54 69.28 72.38 76.89 78.24 PCA OA/% 69.11 76.38 77.48 79.19 81.29 AA/% 77.47 81.82 83.54 84.86 86.32 Kappa/% 61.40 69.84 71.38 73.50 76.04 IFRF OA/% 74.53 86.48 89.87 92.83 93.92 AA/% 69.89 81.89 85.90 89.05 90.74 Kappa/% 67.85 82.50 86.75 90.57 91.97 HiFi OA/% 78.48 85.41 87.52 88.87 89.92 AA/% 82.21 88.29 90.29 91.75 91.90 Kappa/% 72.50 81.18 83.81 85.55 86.68 CCJSR OA/% 61.05 64.65 70.16 75.91 76.23 AA/% 63.17 63.93 68.49 71.60 72.82 Kappa/% 50.82 55.59 61.35 68.63 69.04 R-VCANet OA/% 80.72 86.97 90.67 92.07 93.53 AA/% 87.46 90.97 93.61 94.35 95.36 Kappa/% 75.52 83.17 87.83 89.62 91.58 SSRN OA/% 92.35 94.43 96.49 96.60 98.09 AA/% 92.25 94.04 96.31 96.67 98.01 Kappa/% 89.90 92.60 95.32 95.48 97.47 MSLRR OA/% 90.77 94.68 95.50 96.24 96.78 AA/% 92.76 95.44 96.22 96.91 97.34 Kappa/% 87.91 92.98 94.06 95.03 95.75 MSLRR_TWRF OA/% 94.98 97.43 97.94 98.14 98.31 AA/% 96.28 97.99 98.40 98.67 98.84 Kappa/% 93.42 96.60 97.28 97.54 97.76
Table 7. Classification accuracy of different training samples for Salinas
View tableView in ArticleTable 7. Classification accuracy of different training samples for Salinas
方法 指标 训练样本数 10 20 30 40 50 SVM OA/% 82.71 84.51 85.52 87.24 87.36 AA/% 89.21 90.46 91.77 92.72 92.94 Kappa/% 80.85 82.81 83.93 85.81 85.94 PCA OA/% 82.16 84.98 85.71 86.31 87.05 AA/% 89.19 90.99 91.98 92.41 92.94 Kappa/% 80.20 83.32 84.13 84.79 85.60 IFRF OA/% 95.07 97.98 98.30 98.64 99.05 AA/% 96.48 98.46 98.86 99.01 99.20 Kappa/% 94.52 97.76 98.11 98.49 98.95 HiFi OA/% 87.90 91.33 91.79 92.71 93.09 AA/% 93.68 95.64 96.07 96.59 96.86 Kappa/% 86.61 90.37 90.88 91.88 92.31 CCJSR OA/% 84.94 86.34 89.35 89.92 90.20 AA/% 91.22 91.97 94.09 94.36 94.44 Kappa/% 83.29 84.85 88.17 88.76 89.11 R-VCANet OA/% 89.23 90.39 91.83 93.27 94.25 AA/% 94.34 95.63 96.31 97.06 97.86 Kappa/% 88.04 89.32 90.92 92.52 93.69 SSRN OA/% 90.57 89.83 92.49 95.89 96.94 AA/% 94.96 96.11 97.34 97.72 98.50 Kappa/% 89.50 88.75 91.68 95.42 96.58 MSLRR OA/% 88.94 90.73 90.92 91.74 92.00 AA/% 93.55 95.03 95.35 95.91 96.10 Kappa/% 87.68 89.65 89.88 90.79 91.08 MSLRR_TWRF OA/% 99.23 99.42 99.68 99.74 99.77 AA/% 99.23 99.44 99.58 99.67 99.70 Kappa/% 99.15 99.35 99.64 99.71 99.74
Table 8. Running time of different compared methods
View tableView in ArticleTable 8. Running time of different compared methods
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
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Mei LU, Jiatian LI, Wen LI, Mihong HU, Jiaxin YANG. Fusion of Multiscale Low-rank Representation and Two Way Recursive Filtering for Hyperspectral Image Classification[J]. Remote Sensing Technology and Application, 2024, 39(2): 393
Paper Information
Category: Research Articles
Received: Jul. 5, 2022
Accepted: --
Published Online: Aug. 13, 2024
The Author Email: LU Mei (1848957482@qq.com)
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