Laser & Optoelectronics Progress, Volume. 57, Issue 6, 061013(2020)
Hyperspectral Image Classification Algorithm Based on Space-Spectral Weighted Nearest Neighbor
Figures & Tables(11)
Fig. 1. Process of removing background point. (a) Original image; (b) random sample points; (c) non-nearest neighbor sample points; (d) processing non-nearest neighbor sample points; (e) filtered sample points
Fig. 2. Indian Pines dataset. (a) False-color image; (b) ground-type survey map;(c) spectral curves[14]
Fig. 3. PaviaU dataset. (a) False-color image; (b) ground-type survey map; (c) spectral curves[14]
Fig. 5. OA of different algorithms with different percentages of training samples
Fig. 6. Classification results of different algorithms in Indian Pines dataset. (a) NN; (b) SRC; (c) SVM; (d) WSSD-KNN; (e)SSNN; (f) SSWNN
Fig. 8. OA of different algorithms with different percentages of training samples
Fig. 9. Classification results of different algorithms in PaviaU dataset. (a) NN; (b) SRC; (c) SVM; (d) WSSD-KNN; (e) SSNN; (f) SSWNN
Table 1. Classification accuracy of different classes in Indian Pines dataset for different algorithms
View tableTable 1. Classification accuracy of different classes in Indian Pines dataset for different algorithms
Grade Category Training sample set Test sample set Classification accuracy /% NN SRC SVM WSSD-KNN SSNN SSWNN 1 Alfalfa 10 36 48.28 55.56 69.44 92.11 97.22 100.00 2 Corn-notill 143 1285 58.86 55.98 72.13 89.13 94.17 98.24 3 Corn-min 83 747 51.47 54.03 69.23 84.14 93.84 94.55 4 Corn 24 213 44.15 41.40 57.61 82.76 88.00 99.00 5 Grass/Pasture 48 435 85.30 82.37 86.71 97.03 100.00 98.82 6 Grass/Tress 73 657 84.30 79.92 90.61 98.31 93.94 96.98 7 Grass Pasture mowed 10 18 50.00 60.61 69.23 69.23 76.67 80.77 8 Hay-windrowed 48 430 91.29 93.10 96.52 100.00 98.62 100.00 9 Oats 10 10 16.67 14.29 38.46 62.50 65.22 63.16 10 Soybean-notill 97 875 59.55 58.39 73.99 87.76 95.27 95.74 11 Soybean-min 246 2209 69.40 69.54 81.82 92.36 95.29 96.02 12 Soybean-clean 59 534 48.35 46.72 79.22 82.99 91.62 96.17 13 Wheat 21 184 85.43 86.44 94.27 98.92 94.59 94.38 14 Woods 127 1138 90.54 89.05 93.80 97.82 99.12 97.09 15 Buildings-Grass-Tree-Drives 39 347 40.94 51.44 63.95 91.88 98.18 99.37 16 Stone-steel-towers 10 83 96.34 98.68 98.65 98.80 93.24 94.74 OA 68.70 68.71 80.66 91.74 95.31 96.75 AA 63.80 64.84 77.23 89.11 92.19 94.06 Kappa 0.643 0.642 0.780 0.906 0.947 0.963
Table 2. Classification accuracy of different classes in PaviaU dataset for different algorithms
View tableTable 2. Classification accuracy of different classes in PaviaU dataset for different algorithms
Grade Category Training sample set Test sample set Classification accuracy /% NN SRC SVM WSSD-KNN SSNN SSWNN 1 Asphalt 398 6233 91.61 93.13 93.09 97.91 100.00 99.64 2 Meadows 1119 17530 87.79 87.09 93.23 97.73 99.90 99.49 3 Gravel 126 1973 65.98 65.74 84.46 96.51 61.76 97.11 4 Trees 184 2880 94.34 94.97 95.35 99.57 98.77 97.82 5 Sheets 81 1264 99.42 99.75 99.26 99.59 99.91 96.52 6 Soil 302 4727 71.82 71.93 87.65 96.47 99.78 99.49 7 Bitumen 80 1250 69.08 68.04 87.57 91.46 77.68 98.18 8 Bricks 221 3461 65.22 66.95 79.19 93.68 96.08 93.59 9 Shadows 57 890 99.75 99.88 99.40 99.65 96.13 96.34 OA 83.83 83.91 91.24 97.21 95.57 98.54 AA 82.78 83.05 91.02 96.95 92.22 97.57 Kappa 0.783 0.784 0.883 0.963 0.941 0.981
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Lei Ji, Xin Zhang, Limei Zhang, Zhang Wen. Hyperspectral Image Classification Algorithm Based on Space-Spectral Weighted Nearest Neighbor[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061013
Paper Information
Category: Image Processing
Received: Aug. 23, 2019
Accepted: Sep. 15, 2019
Published Online: Mar. 6, 2020
The Author Email: Xin Zhang (xzhang1@gzu.edu.cn)
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