Laser & Optoelectronics Progress, Volume. 57, Issue 6, 061017(2020)
Hyperspectral Remote Sensing Image Classification Algorithm Based on Nonlocal Mode Feature Fusion
Figures & Tables(10)
Fig. 2. Hyperspectral two-level classification model for nonlocal mode feature fusion
Fig. 3. Classification results. (a) Classification results combined with local neighborhood information; (b) classification results combined with nonlocal mode feature fusion
Fig. 4. Contrast figures before and after algorithm correction. (a) Before correction; (b) after correction
Fig. 5. Classification results of Pavia University dataset obtained by different algorithms. (a) Original image; (b) true classification picture; (c) SVM; (d) CK-SVM; (e) SOMP; (f) MASR; (g) S-SAE; (h) our method
Fig. 6. Classification results of Indian Pines dataset obtained by different algorithms. (a) Original image; (b) true classification picture; (c) SVM; (d) CK-SVM; (e) SOMP; (f)MASR; (g) S-SAE; (h) our method
Fig. 7. Effect of number of training samples of different data on OA in different algorithms. (a) Pavia University; (b) Indian Pines
Table 1. Relationship between imported label correctness rate and thresholdunit: %
View tableTable 1. Relationship between imported label correctness rate and thresholdunit: %
Dataset T 0.96 0.97 0.98 0.99 0.999 0.9999 Indian Pines 86.95 87.54 88.46 90.14 95.37 97.86 Pavia University 93.95 94.52 95.16 96.14 98.55 99.12
Table 2. Experimental data and classification accuracies of the Pavia University dataset
View tableTable 2. Experimental data and classification accuracies of the Pavia University dataset
Class Number of samples Classification accuracy /% Train Test SVM CK-SVM SOMP MASR S-SAE Our method Asphalt 200 6431 80.50 97.90 82.11 77.26 84.48 97.41 Meadows 200 18449 84.48 98.95 95.50 96.62 93.61 99.62 Gravel 200 1899 78.91 93.77 98.11 99.18 83.89 99.25 Trees 200 2864 96.24 98.96 96.24 96.91 98.43 98.64 Painted metal sheets 200 1145 99.74 100 99.06 100 100 100 Bare soil 200 4829 83.96 97.06 98.55 98.74 86.25 100 Bitumen 200 1130 91.39 99.56 98.34 99.99 87.79 100 Self-blocking bricks 200 3482 81.27 96.45 94.90 96.18 91.50 98.69 Shadows 200 747 98.44 99.87 88.44 83.59 99.74 99.95 OA/% 84.98 98.16 93.93 93.86 91.15 98. 61 Kappa 0.80 0.98 0.92 0.92 0.88 0.98
Table 3. Experimental data and classification accuracies of the Indian Pines dataset
View tableTable 3. Experimental data and classification accuracies of the Indian Pines dataset
Class Number of samples Classification accuracy /% Train Test SVM CK-SVM SOMP MASR S-SAE Our method Alfalfa 5 41 60.47 74.42 83.72 88.83 68.83 93.56 Corn-notill 143 1285 68.53 84.75 90.58 98.24 82.18 97.13 Corn-mintill 83 747 59.01 85.03 90.10 97.82 79.03 96.49 Corn 23 214 38.22 86.67 92.89 95.07 81.91 97.57 Grass-pasture 50 433 92.16 100 91.27 97.48 95.99 96.23 Grass-trees 75 655 86.56 96.97 93.22 99.59 94.63 98.91 Grass-pasture-mowed 3 25 53.85 76.92 84.62 99.29 74.21 98.82 Hay-windrowed 49 429 94.26 99.78 99.78 99.96 98.19 98.99 Oats 2 18 22.22 94.44 44.44 69.00 92.86 94.34 Soybeans-notill 97 875 67.06 77.46 85.82 97.81 83.14 95.06 Soybeans-mintill 247 2208 73.17 93.41 94.86 98.63 92.47 98.03 Soybeans-clean 61 532 50.18 95.55 78.86 98.29 76.63 97.14 Wheat 21 184 94.85 99.48 90.21 98.74 99.25 99.40 Woods 129 1136 88.19 97.59 99.33 100 95.10 100 Bldg-grass-trees-drives 38 348 57.22 92.37 75.20 96.79 90.58 97.19 Stone-steel-towers 10 83 77.53 98.88 86.36 95.08 83.54 98.96 OA/% 73.01 91.36 91.46 97.41 88.67 97.35 Kappa 0.69 0.90 0.90 0.97 0.87 0.97
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Hongchao Liu, Anguo Dong. Hyperspectral Remote Sensing Image Classification Algorithm Based on Nonlocal Mode Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061017
Paper Information
Category: Image Processing
Received: Jul. 23, 2019
Accepted: Aug. 29, 2019
Published Online: Mar. 6, 2020
The Author Email: Anguo Dong (18710866110@163.com)
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