Acta Optica Sinica, Volume. 37, Issue 8, 0828005(2017)
Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation
Figures & Tables(10)
Fig. 1. Hyperspectral images. (a) Category of real ground; (b) classification result of OMP algorithm; (c) spectral curves
Fig. 2. Clustering results of ground in the neighborhood. (a) (129,35); (b) (96,39); (c) (38,52); (d) (100,58)
Fig. 3. Contrast figures before and after algorithm correction. (a) Before correction; (b) after correction
Fig. 4. Classification results of Pavia University dataset obtained by different algorithms. (a) Original image; (b) real ground; (c) SVM algorithm; (d) CK-SVM algorithm; (e) OMP algorithm; (f) SOMP algorithm; (g) MASR algorithm; (h) SC-SOMP algorithm
Fig. 5. Classification results of Indian Pines dataset obtained by different algorithms. (a) Original image; (b) real ground; (c) SVM algorithm; (d) CK-SVM algorithm; (e) OMP algorithm; (f) SOMP algorithm; (g) MASR algorithm; (h) SC-SOMP algorithm
Fig. 6. Classification results of Salinas Valley dataset obtained by different algorithms. (a) Original image; (b) real ground; (c) SVM algorithm; (d) CK-SVM algorithm; (e) OMP algorithm; (f) SOMP algorithm; (g) MASR algorithm; (h) SC-SOMP algorithm
Fig. 7. Effect of the number of training samples. (a) Pavia University; (b) Indian Pines; (c) Salinas Valley
Table 1. Experimental data and classification accuracies of the Pavia University dataset
View tableTable 1. Experimental data and classification accuracies of the Pavia University dataset
Class Sample Classification algorithm Train Test SVM CK-SVM OMP SOMP MASR SC-SOMP Asphalt 250 6381 80.50 97.90 49.72 66.27 77.26 91.87 Meadows 250 18339 84.48 98.95 62.36 92.32 96.62 99.11 Gravel 250 1849 78.91 93.77 63.00 96.76 99.18 99.78 Trees 250 2814 96.24 98.96 84.49 94.94 96.91 98.33 Painted metal sheets 250 1095 99.74 100.00 99.12 99.13 100.00 99.36 Bare soil 250 4779 83.96 97.06 54.12 92.73 98.74 100.00 Bitumen 250 1080 91.39 99.56 83.46 99.16 99.99 99.91 Self-blocking bricks 250 3432 81.27 96.44 62.27 90.06 96.18 98.08 Shadows 250 697 98.44 99.87 95.18 85.02 83.59 89.67 OA /% 84.98 98.16 63.55 88.77 93.86 98.03 Kappa 0.80 0.98 0.54 0.85 0.92 0.97
Table 2. Classification accuracies of Indian Pines dataset obtained by different algorithms
View tableTable 2. Classification accuracies of Indian Pines dataset obtained by different algorithms
Class Classification algorithm SVM CK-SVM OMP SOMP MASK SC-SOMP OA /% 77.64 94.86 61.01 90.88 98.41 98.37 Kappa 0.74 0.94 0.66 0.90 0.98 0.97
Table 3. Classification accuracies of Salinas Valley dataset obtained by different algorithms
View tableTable 3. Classification accuracies of Salinas Valley dataset obtained by different algorithms
Class Classification algorithm SVM CK-SVM OMP SOMP MASR SC-SOMP OA /% 86.29 94.56 82.17 88.81 88.04 98.34 Kappa 0.85 0.94 0.8 0.88 0.87 0.97
Tools
Get Citation
Copy Citation Text
Anguo Dong, Jiaxun Li, Bei Zhang, Miaomiao Liang. Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005
Paper Information
Category: Remote Sensing and Sensors
Received: Mar. 29, 2017
Accepted: --
Published Online: Sep. 7, 2018
The Author Email: Li Jiaxun (15637793688@163.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20