Laser & Optoelectronics Progress, Volume. 60, Issue 2, 0228010(2023)
Hyperspectral Remote Sensing Image Classification Model Based on S2AF-GCN
Figures & Tables(20)
Fig. 1. Classification model of hyperspectral remote sensing images based on S2AF-GCN
Fig. 2. Renderings of spatial neighborhood feature aggregation. (a) European distance calculated by original features; (b) European distance calculated by aggregation features
Fig. 4. Comparison of composition based on original features and aggregation features. (a) Random samples; (b) 5 nearest neighbors from original features; (c) 5 nearest neighbors from aggregation features
Fig. 6. Classification results on Indian Pines dataset. (a) False color; (b) ground truth; (c) FuNet-C; (d) S2GCN; (e) GCN(OF); (f) GCN(AF); (g) S2AF-GCN(OF); (h) S2AF-GCN(AF)
Fig. 7. Classification results on Pavia University dataset. (a) False color; (b) ground truth; (c) FuNet-C; (d) S2GCN; (e) GCN(OF); (f) GCN(AF); (g) S2AF-GCN(OF); (h) S2AF-GCN(AF)
Fig. 8. Classification results on Kennedy Space Center dataset. (a) False color; (b) ground truth; (c) FuNet-C; (d) S2GCN; (e) GCN(OF); (f) GCN(AF); (g) S2AF-GCN(OF); (h) S2AF-GCN(AF)
Fig. 9. Influence of nearest neighbor number K on the overall accuracy on different datasets. (a) Indian Pines; (b) Pavia University; (c) Kennedy Space Center
Fig. 10. OA of different models under different proportions of training samples. (a) Indian Pines; (b) Pavia University; (c) Kennedy Space Center
Table 1. Land cover category and dataset division on Indian Pines dataset
View tableTable 1. Land cover category and dataset division on Indian Pines dataset
Class No. Class name Number of samples Train Test Total 105（1%） 10261 10366 1 Corn Notill 14 1420 1434 2 Corn Mintill 8 826 834 3 Corn 3 231 234 4 Grass Pasture 5 492 497 5 Grass Trees 7 740 747 6 Hay Windrowed 5 484 489 7 Soybean Notill 9 959 968 8 Soybean Mintill 24 2444 2468 9 Soybean Clean 6 608 614 10 Wheat 2 210 212 11 Woods 12 1282 1294 12 Buildings Grass Trees Drives 4 376 380 13 Stone Steel Towers 2 93 95 14 Alfalfa 2 52 54 15 Grass Pasture Mowed 1 25 26 16 Oats 1 19 20
Table 2. Land cover category and dataset division on Pavia University dataset
View tableTable 2. Land cover category and dataset division on Pavia University dataset
Class No. Class name Number of samples Train Test Total 427（1%） 42349 42776 1 Asphalt 66 6565 6631 2 Meadows 186 18463 18649 3 Gravel 21 2078 2099 4 Trees 31 3033 3064 5 Metal Sheets 13 1332 1345 6 Bare Soil 50 4979 5029 7 Bitumen 13 1317 1330 8 Bricks 37 3645 3682 9 Shadows 10 937 947
Table 3. Land cover category and dataset division on Kennedy Space Center dataset
View tableTable 3. Land cover category and dataset division on Kennedy Space Center dataset
Class No. Class name Number of samples Train Test Total 52（1%） 5159 5211 1 Scrub 8 753 761 2 Willow swamp 2 241 243 3 CP hammock 3 253 256 4 Slash pine 3 249 252 5 Oak/Broadleaf 2 159 161 6 Hardwood 2 227 229 7 Swap 1 104 105 8 Graminoid marsh 4 427 431 9 Spartina marsh 5 515 520 10 Cattail marsh 4 400 404 11 Salt marsh 4 415 419 12 Mud flats 5 498 503 13 Water 9 918 927
Table 4. Comparison of each method
View tableTable 4. Comparison of each method
Model CNN feature extraction Information used Features used in constructing graph Adjacency matrix FuNet-C 2DCNN Spatial and spectral information Original spectral features Inaccurate S2GCN No Spatial and spectral information Original spectral features Inaccurate S2AF-GCN No Spatial and spectral information Aggregation features Accurate
Table 5. Comparison of similarities and differences of various methods in ablation experiment
View tableTable 5. Comparison of similarities and differences of various methods in ablation experiment
Model Classify using original features Classify using aggregation features Construct adjacency matrix using original features Construct adjacency matrix using aggregation features Accurate adjacency matrix GCN（OF） √ √ × GCN（AF） √ √ × S2AF-GCN（OF） √ √ √ S2AF-GCN（AF） √ √ √
Table 6. Classification results on Indian Pines dataset
View tableTable 6. Classification results on Indian Pines dataset
Class No. FuNet-C S2GCN GCN（OF） GCN（AF） S2AF-GCN（OF） S2AF-GCN（AF） 1 81.20 83.10 61.27 67.11 81.83 82.18 2 76.15 81.48 24.46 57.51 55.57 85.84 3 89.61 98.27 39.83 47.62 51.52 60.17 4 78.66 78.05 73.37 74.19 82.11 78.46 5 71.76 92.43 85.54 87.43 89.05 99.05 6 99.38 100.00 94.42 97.11 99.79 100.00 7 73.30 82.59 61.94 87.17 78.83 88.22 8 61.46 75.16 57.90 74.02 64.57 81.18 9 46.88 78.13 31.41 39.80 59.05 70.07 10 99.52 100.00 91.90 99.05 100.00 100.00 11 96.80 99.22 95.16 94.38 97.58 98.75 12 57.18 66.76 38.83 71.81 66.22 67.82 13 83.87 86.02 80.65 80.65 77.42 88.17 14 92.31 84.62 32.69 82.69 76.92 94.23 15 72.00 92.00 36.00 92.00 80.00 100.00 16 42.11 57.89 47.37 73.68 63.16 68.42 OA /% 74.99 84.08 63.19 75.53 76.48 85.51 AA /% 76.39 84.73 59.55 76.64 76.34 85.16 Kappa 0.7181 0.8196 0.5823 0.7226 0.7317 0.8353
Table 7. Classification results on Pavia University dataset
View tableTable 7. Classification results on Pavia University dataset
Class No. FuNet-C S2GCN GCN（OF） GCN（AF） S2AF-GCN（OF） S2AF-GCN（AF） 1 85.73 95.55 88.35 91.32 91.93 94.58 2 99.20 98.26 95.79 95.72 98.60 99.13 3 77.53 79.40 68.62 62.03 77.53 82.24 4 57.73 86.55 90.74 92.35 89.85 89.99 5 99.47 100.00 95.35 99.40 99.55 100.00 6 75.34 89.54 74.11 89.27 90.72 98.77 7 96.13 56.42 61.73 88.84 92.10 99.92 8 85.27 91.85 82.36 97.31 95.58 98.86 9 99.89 100.00 99.15 100.00 100.00 100.00 OA /% 89.00 93.29 88.24 92.52 94.58 96.95 AA /% 86.25 88.62 84.02 90.69 92.87 95.43 Kappa 0.8511 0.9103 0.8429 0.9009 0.9278 0.9561
Table 8. Classification results on Kennedy Space Center dataset
View tableTable 8. Classification results on Kennedy Space Center dataset
Class No. FuNet-C S2GCN GCN（OF） GCN（AF） S2AF-GCN（OF） S2AF-GCN（AF） 1 98.41 100.00 92.96 96.95 95.22 98.94 2 100.00 100.00 87.97 94.19 98.76 98.34 3 45.06 100.00 82.21 90.91 70.36 100.00 4 6.83 59.84 17.27 58.63 64.26 72.69 5 90.57 100.00 61.01 62.26 92.45 97.48 6 22.47 62.56 50.66 59.03 64.32 92.95 7 40.38 69.23 37.50 46.15 40.38 76.92 8 83.84 100.00 88.52 91.57 94.38 100.00 9 83.69 83.69 84.85 83.11 89.90 83.69 10 96.50 94.00 71.00 93.75 80.25 94.00 11 100.00 100.00 80.24 87.47 87.23 97.11 12 96.50 96.39 94.58 95.78 95.38 96.39 13 100.00 100.00 99.78 99.46 100.00 100.00 OA /% 84.09 93.35 82.05 88.41 88.58 94.92 AA /% 74.16 89.67 72.97 81.48 82.53 92.96 Kappa 0.8228 0.9259 0.7999 0.8707 0.8727 0.9435
Table 9. Running time of FuNet-C, S2GCN, S2AF-GCN models on three datasets
View tableTable 9. Running time of FuNet-C, S2GCN, S2AF-GCN models on three datasets
Dataset FuNet-C S2GCN S2AF-GCN Indian Pines 420 117 182 Pavia University 963 133 240 Kennedy Space Center 280 40 56
Table 10. Classification results of S2AF-GCN on different datasets under different aggregation times
View tableTable 10. Classification results of S2AF-GCN on different datasets under different aggregation times
m+1 Indian Pines Pavia University Kennedy Space Center OA /% AA /% Kappa OA /% AA /% Kappa OA /% AA /% Kappa 2 77.52 80.36 0.7451 93.98 92.34 0.9200 90.83 85.29 0.8978 4 81.56 82.24 0.7912 95.89 94.57 0.9455 92.61 87.54 0.9177 6 84.81 84.01 0.8276 96.42 95.52 0.9526 93.47 89.09 0.9272 8 85.51 85.16 0.8353 96.65 95.43 0.9561 94.92 92.96 0.9435 10 85.05 85.19 0.8304 96.69 95.94 0.9595 92.44 87.65 0.9157 12 83.74 85.14 0.8156 95.96 93.41 0.9462 93.14 89.12 0.9236
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Hailin Song, Xili Wang. Hyperspectral Remote Sensing Image Classification Model Based on S2AF-GCN[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228010
Paper Information
Category: Remote Sensing and Sensors
Received: Jan. 24, 2022
Accepted: Mar. 14, 2022
Published Online: Feb. 7, 2023
The Author Email: Xili Wang (wangxili@snnu.edu.cn)
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