Laser & Optoelectronics Progress, Volume. 56, Issue 15, 151006(2019)
Hyperspectral Image Classification Based on Residual Dense Network
Figures & Tables(16)
Fig. 4. Illustration of residual dense network model for hyperspectral image classification
Table 1. Numbers of Indian Pines data samples
View tableTable 1. Numbers of Indian Pines data samples
Number 1 2 3 4 5 6 7 8 9 10 Category Corn- notill Corn- mintill Grass- pasture Grass- trees Hay- windowed Soybean- notill Soybean- mintill Soybean- clean Woods Total Number of training sample 200 200 200 200 200 200 200 200 200 1800 Number of testing sample 1228 630 283 530 278 772 2255 393 1065 7434
Table 2. Numbers of University of Pavia data samples
View tableTable 2. Numbers of University of Pavia data samples
Number 1 2 3 4 5 6 7 8 9 Category Asphalt Meadows Gravel Trees Sheets Bare Soil Bitumen Bricks Shadows Total Number of training sample 200 200 200 200 200 200 200 200 200 1800 Number of testing sample 6431 18449 1899 2864 1145 4829 1130 3482 747 40976
Table 3. Numbers of Salinas data samples
View tableTable 3. Numbers of Salinas data samples
Number Category Number of training sample Number of testing sample 1 Baocoli_weeds_1 200 1809 2 Baocoli_weeds_2 200 3526 3 Fallow 200 1776 4 Fallow_rough_plow 200 1194 5 Fallow_smooth 200 2478 6 Stubble 200 3759 7 Celery 200 3379 8 Grapes_untrained 200 11071 9 Soil_vinyard_develop 200 6003 10 Corn_senesced_weeds 200 3078 11 Lettuce_romaine_4 weeks 200 868 12 Lettuce_romaine_5 weeks 200 1727 13 Lettuce_romaine_6 weeks 200 716 14 Lettuce_romaine_7 weeks 200 870 15 Vinyard_untrained 200 7068 16 Vinyard_vertical_trellis 200 1607 Total 3200 50929
Table 4. Classification accuracies (mean value±variance) of experimental datasets
View tableTable 4. Classification accuracies (mean value±variance) of experimental datasets
Dataset Criteria /% SVM CNN ResNet DenseNet ResDenNet OA 86.82±1.12 96.09±0.46 97.79±0.47 97.92±0.11 98.71±0.01 IN AA 87.60±0.43 96.28±0.31 97.90±0.45 98.09±0.13 98.94±0.01 Kappa 84.70±1.34 95.44±0.63 97.42±0.63 97.56±0.15 98.48±0.02 OA 89.87±1.25 97.33±0.03 98.49±0.19 98.58±0.09 99.31±0.01 UP AA 89.91±0.51 96.55±0.04 98.26±0.16 98.43±0.07 99.08±0.02 Kappa 87.32±1.46 96.48±0.06 98.01±0.34 98.13±0.16 99.08±0.01 OA 89.66±1.18 92.84±0.52 96.39±0.54 96.52±0.15 97.91±0.02 SA AA 93.62±0.47 96.44±0.24 98.16±0.31 98.13±0.09 98.90±0.01 Kappa 88.56±1.26 92.05±0.35 95.99±0.42 96.13±0.17 97.68±0.03
Table 5. Classification accuracies (mean value±variance) of Salinas dataset
View tableTable 5. Classification accuracies (mean value±variance) of Salinas dataset
Dataset Criteria/% Model based on IN Model based on UP Model self-trained OA 96.75±0.13 97.21±0.10 97.91±0.02 SA AA 98.52±0.01 98.67±0.01 98.90±0.01 Kappa 96.38±0.16 96.90±0.12 97.68±0.03
Table 6. Training and testing time for different methodss
View tableTable 6. Training and testing time for different methodss
Dataset Type CNN ResNet DenseNet ResDenNet IN Train 132.32 231.49 136.07 249.43 Test 1.98 6.56 3.91 4.82 UP Train 94.87 156.80 192.23 187.68 Test 12.29 14.44 17.08 16.89 SA Train 173.84 300.37 354.32 272.12 Test 7.26 9.23 14.27 15.09
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Xiangpo Wei, Xuchu Yu, Xiong Tan, Bing Liu. Hyperspectral Image Classification Based on Residual Dense Network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 151006
Paper Information
Category: Image Processing
Received: Jan. 3, 2019
Accepted: Mar. 6, 2019
Published Online: Aug. 5, 2019
The Author Email: Xiangpo Wei (13526635671@163.com)
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