Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0210014(2022)
Hyperspectral Image Classification Based on Modified DenseNet and Spatial Spectrum Attention Mechanism
Figures & Tables(18)
Fig. 3. Model of the spatial spectrum attention mechanism. (a) Channel attention mechanism; (b) spatial attention mechanism
Fig. 7. Pavia University data set and label. (a) Pavia University data set; (b) label
Fig. 9. Classification results of the Indian Pines data set. (a) Indian Pines data set; (b) label; (c) 2D_CNN; (d) 3D_CNN; (e) M3RCNN; (f) 3D_DenseNet; (g) MDSSAN
Fig. 10. Classification results of the Pavia University data set. (a) Pavia University data set; (b) label; (c) 2D_CNN;(d) 3D_CNN; (e) M3RCNN; (f) 3D_DenseNet; (g) MDSSAN
Fig. 11. Classification result diagram of the KSC data set. (a) KSC data set; (b) label; (c) 2D_CNN; (d) 3D_CNN; (e) M3RCNN; (f) 3D_DenseNet; (g) MDSSAN
Table 1. Number of samples of training set, verification set and test set selected from Indian Pines data set
View tableTable 1. Number of samples of training set, verification set and test set selected from Indian Pines data set
No. Category Training set Verification set Test set Total 2164 1182 7311 1 alfalfa 11 4 34 2 corn-notill 284 141 1009 3 corn-min 167 100 572 4 corn 58 28 163 5 grass/pasture 103 75 320 6 grass/trees 148 70 530 7 grass/pasture-mowed 13 11 25 8 hay-windrowed 106 51 345 9 oats 12 12 23 10 soybeans-notill 202 111 689 11 soybeans-min 509 242 1737 12 soybeans-clean 126 73 430 13 wheat 55 26 163 14 woods 250 155 902 15 building-grass-trees-drives 85 55 291 16 stone-steel towers 35 28 78
Table 2. Number of samples of training set, verification set and test set selected from Pavia University data set
View tableTable 2. Number of samples of training set, verification set and test set selected from Pavia University data set
No. Category Training set Verification set Test set Total 8514 4408 29989 1 asphalt 1306 691 4637 2 meadows 3730 1902 13023 3 gravel 400 225 1483 4 trees 626 336 2114 5 sheets 244 155 961 6 bare soil 1033 474 3540 7 bitumen 282 128 941 8 bricks 714 405 2587 9 shadows 179 92 703
Table 3. Number of samples of training set, verification set and test set selected from KSC data set
View tableTable 3. Number of samples of training set, verification set and test set selected from KSC data set
No. Category Training set Verification set Test set Total 1122 623 3739 1 scrub 172 70 522 2 willow swamp 50 32 167 3 camping hammock 52 28 185 4 slash pine 66 26 172 5 oak/broadleaf 39 19 118 6 hardwood 49 32 166 7 swap 23 24 79 8 graminoid marsh 81 62 312 9 spartina marsh 106 55 386 10 cattail marsh 88 44 302 11 salt marsh 90 58 304 12 mud flats 103 66 370 13 water 203 107 656
Table 4. Classification results of the Indian Pines data set
View tableTable 4. Classification results of the Indian Pines data set
Classification 2D_CNN 3D_CNN M3RCNN 3D_DenseNet MDSSAN Alfalfa /% 93.48 100.00 71.74 97.83 97.83 Corn-notill /% 95.87 99.86 85.92 95.59 98.32 Corn-min /% 93.13 90.84 93.86 100.00 99.76 Corn /% 92.83 94.09 92.41 99.58 100.00 Grass/pasture /% 81.78 98.55 93.37 95.65 99.79 Grass/trees /% 98.49 98.77 98.49 99.73 99.86 Grass/pasture-mowed /% 89.29 100.00 100.00 100.00 100.00 Hay-windrowed /% 99.58 100.00 100.00 100.00 100.00 Oats /% 90.00 85.00 55.00 40.00 80.00 Soybeans-notill /% 95.88 89.51 83.54 99.59 99.38 Soybeans-min /% 98.21 83.95 97.84 99.96 99.76 Soybeans-clean /% 96.46 98.48 93.76 97.64 99.49 Wheat /% 98.05 100.00 99.51 94.15 100.00 Woods /% 99.60 99.60 98.97 99.45 99.92 Building-grass-trees-drives /% 97.93 98.19 97.15 99.22 98.45 Stone-steel towers /% 97.85 98.92 82.80 96.77 96.77 OA /% 96.43 93.86 93.89 98.59 99.43 AA /% 94.90 95.99 90.27 94.70 98.08 Kappa coefficient 95.93 93.03 93.01 98.40 99.35 Time /min 1.89 5.38 32.27 18.07 12.43
Table 5. Classification results of the Pavia University data set
View tableTable 5. Classification results of the Pavia University data set
Classification 2D_CNN 3D_CNN M3RCNN 3D_DenseNet MDSSAN Asphalt /% 98.97 99.40 99.26 98.66 99.82 Meadows /% 99.43 99.65 99.98 99.92 99.96 Gravel /% 97.28 94.33 97.14 99.29 97.76 Trees /% 99.71 97.78 99.61 98.47 99.45 Sheets /% 99.70 98.88 100.00 100.00 99.93 Baresoil /% 99.64 99.98 87.09 100.00 99.96 Bitumen /% 84.21 98.87 98.87 99.02 99.32 Bricks /% 97.91 99.35 99.57 100.00 99.78 Shadows /% 91.66 99.89 100.00 97.99 99.37 OA /% 98.53 99.18 98.12 99.54 99.74 AA /% 96.50 98.68 97.95 99.26 99.48 Kappa coefficient 98.05 98.92 97.49 99.39 99.66 Time /min 6.61 7.21 73.92 76.10 19.58
Table 6. Classification results of the KSC data set
View tableTable 6. Classification results of the KSC data set
Classification 2D_CNN 3D_CNN M3RCNN 3D_DenseNet MDSSAN Scrub /% 97.77 99.74 99.74 98.16 100.00 Willow swamp /% 78.60 99.18 93.42 88.48 95.06 CP hammock /% 87.89 93.75 98.83 98.83 100.00 Slash pine /% 70.24 58.33 86.51 88.89 95.24 Oak/broadleaf /% 78.88 80.12 85.09 93.17 93.79 Hardwood /% 93.89 80.34 93.01 92.14 100.00 Swap /% 98.10 78.10 97.14 100.00 100.00 Graminoid marsh /% 89.33 99.54 98.84 99.07 98.84 Spartina marsh /% 90.77 98.65 100.00 99.81 99.62 Cattail marsh /% 90.84 99.26 100.00 100.00 98.51 Salt marsh /% 99.76 100.00 99.52 99.76 100.00 Mud flats /% 99.40 97.02 94.63 97.22 98.81 Water /% 99.89 100.00 99.89 100.00 100.00 OA /% 93.07 95.16 97.45 97.66 98.98 AA /% 90.41 91.08 95.89 96.58 98.45 Kappa coefficient 92.28 94.61 97.16 97.39 98.87 Time /min 1.11 1.74 18.04 11.25 8.19
Table 7. Effect of three-dimensional convolution module before and after the improvement
View tableTable 7. Effect of three-dimensional convolution module before and after the improvement
Data set Model parameter OA/% Before improvement After improvement Before improvement After improvement Indian Pines 1186044 565167 98.70 99.43 Pavia University 1184749 563872 99.17 99.74 KSC 1185489 564612 95.93 98.98
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Xin Wang, Yanguo Fan. Hyperspectral Image Classification Based on Modified DenseNet and Spatial Spectrum Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0210014
Paper Information
Category: Image Processing
Received: Jan. 21, 2021
Accepted: Mar. 15, 2021
Published Online: Dec. 23, 2021
The Author Email: Wang Xin (3166588225@qq.com)
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