Laser & Optoelectronics Progress, Volume. 56, Issue 19, 192801(2019)
Hyperspectral Remote Sensing Image Classification Based on Auto-Encoder
Figures & Tables(12)
Fig. 3. Hyperspectral remote sensing images. (a) True classification picture; (b) classification result of S-SAE algorithm; (c) spectral curves
Fig. 4. Spatial-spectral feature extraction method based on rotation invariant property
Fig. 5. Hyperspectral neighborhood information. (a) Spatial position; (b) magnified picture; (c) neighborhood information of point E; (d) neighborhood information of point F
Fig. 6. Classification algorithm framework for deep learning combined with spatial-spectral information
Fig. 7. Selection of parameters. (a) Selection of number of principal components; (b) selection of window size
Fig. 8. Classification results of Pavia University dataset obtained by different algorithms. (a) Original image; (b) true classification picture; (c) SVM; (d) CK-SVM; (e) OMP; (f) SOMP; (g) proposed method (unselect); (h) proposed method
Fig. 9. Classification results of Indian Pines dataset obtained by different algorithms. (a) Original image; (b) true classification picture; (c) SVM; (d) CK-SVM; (e) OMP; (f) SOMP; (g) proposed method (unselect); (h) proposed method
Fig. 10. Effect of number of training samples on overall accuracy of different datasets. (a) Pavia University; (b) Indian Pines
Table 1. Experimental data and classification accuracy of the Pavia University dataset
View tableTable 1. Experimental data and classification accuracy of the Pavia University dataset
Class Number of samples Classification accuracy /% Train Test SVM CK-SVM OMP SOMP Our method (unselect) Our method Asphalt 200 6431 80.50 97.90 61.20 82.11 93.11 98.10 Meadows 200 18449 84.48 98.95 79.47 95.50 96.11 97.32 Gravel 200 1899 78.91 93.77 68.01 98.11 95.22 97.17 Trees 200 2864 96.24 98.96 91.95 96.24 93.74 99.35 Painted metal sheets 200 1145 99.74 100.00 99.22 99.06 100.00 100.00 Bare soil 200 4829 83.96 97.06 69.84 98.55 96.91 99.28 Bitumen 200 1130 91.39 99.56 84.39 98.34 98.81 99.51 Self-blocking bricks 200 3482 81.27 96.45 76.52 94.90 96.39 96.43 Shadows 200 747 98.44 99.87 98.04 88.44 97.99 100.00 OA /% 84.98 98.16 76.60 93.93 95.88 97.87 Kappa 0.80 0.98 0.70 0.92 0.94 0.97
Table 2. OA and Kappa coefficient of the Indian Pines dataset obtained by different algorithms
View tableTable 2. OA and Kappa coefficient of the Indian Pines dataset obtained by different algorithms
Parameter Classification algorithm SVM CK-SVM OMP SOMP Our method (unselect) Our method OA /% 73.01 91.36 65.87 91.46 90.18 93.99 Kappa coefficient 0.69 0.90 0.61 0.90 0.89 0.93
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Anguo Dong, Hongchao Liu, Qian Zhang, Miaomiao Liang. Hyperspectral Remote Sensing Image Classification Based on Auto-Encoder[J]. Laser & Optoelectronics Progress, 2019, 56(19): 192801
Paper Information
Category: Remote Sensing and Sensors
Received: Mar. 10, 2019
Accepted: Apr. 11, 2019
Published Online: Oct. 23, 2019
The Author Email: Dong Anguo (donganguo@chd.edu.cn), Liu Hongchao (18710866110@163.com)
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