Acta Photonica Sinica, Volume. 50, Issue 4, 241(2021)
Feature Extraction of Hyperspectral Image Based on Sparse Representation and Learning Graph Regularity
Figures & Tables(13)
Fig. 4. Classification results of PaviaU dataset with different algorithms
Fig. 5. 3D projection graph of PaviaU dataset with different algorithms
Fig. 6. Classification results of Indian Pines dataset with different algorithms
Table 1. Classification performance of PaviaU dataset with various methods（%）
View tableView in ArticleTable 1. Classification performance of PaviaU dataset with various methods（%）
1⁃NN SVM Method OSF PCA LPP LDMGI SPA MSME LESR LMSDL LDGSPP OSF PCA LPP LDMGI SPA MSME LESR LMSDL LDGSPP Class1 70.2 70.4 74.2 73.8 71.0 69.2 74.4 81.8 78.2 75.8 78.2 81.5 78.9 77.8 74.6 79.3 89.7 92.3 Class2 70.1 70.2 72.0 70.9 70.8 67.6 71.8 78.1 91.6 79.0 79.5 70.5 69.6 80.9 70.4 85.2 82.1 94.9 Class3 68.3 68.2 73.0 65.0 70.1 63.8 67.5 78.1 80.9 84.4 82.5 80.9 73.7 83.7 82.1 84.7 84.6 78.4 Class4 90.9 90.9 90.6 93.4 91.5 91.0 92.3 92.5 93.1 92.4 90.1 90.8 96.0 92.6 93.2 94.2 96.2 96.3 Class5 98.9 98.9 99.8 99.9 98.9 98.4 99.5 100 100 99.5 99.5 100 99.9 99.6 99.5 99.6 100 100 Class6 70.0 69.9 75.1 70.3 67.4 67.7 71.1 78.7 94.8 80.7 82.9 83.2 89.0 83.7 79.8 85.5 84.6 97.7 Class7 90.5 90.2 83.2 89.8 89.4 87.2 88.7 92.5 97.8 92.2 92.0 81.1 91.8 93.1 91.7 92.9 91.8 98.5 Class8 70.5 70.5 69.0 72.1 69.2 67.5 70.4 77.0 77.1 76.7 76.6 75.8 77.9 75.8 75.8 80.7 83.2 88.6 Class9 99.8 99.9 99.9 99.8 99.8 100 99.8 99.7 100 99.5 99.5 99.7 99.8 99.7 99.9 99.7 99.8 100 AA（%） 81.0 81.0 81.9 81.7 80.8 79.2 81.7 86.5 90.4 86.7 86.8 84.8 86.3 87.4 85.2 89.1 90.2 94.1 OA（%） 73.6 73.7 75.6 74.7 73.7 71.6 75.2 81.3 88.8 81.2 81.8 77.9 78.3 82.6 76.9 85.5 86.0 94.0 KA（%） 66.5 66.6 68.9 60.8 66.6 64.0 68.4 75.9 85.4 75.8 76.5 72.1 72.7 77.6 70.8 81.2 81.9 92.0
Table 2. Classification performance of Indian Pines dataset with various methods（%）
View tableView in ArticleTable 2. Classification performance of Indian Pines dataset with various methods（%）
1⁃NN SVM Method OSF PCA LPP LDMGI SPA MSME LESR LMSDL LDGSPP OSF PCA LPP LDMGI SPA MSME LESR LMSDL LDGSPP Class1 48.3 41.9 48.4 41.9 64.5 48.4 64.5 64.5 77.4 35.4 48.4 58.1 64.5 80.6 61.3 80.6 64.5 87.1 Class2 43.1 41.8 43.9 35.2 43.8 41.5 43.5 59.4 60.8 52.1 51.6 60.3 47.2 61.5 54.9 60.5 75.4 85.2 Class3 53.3 52.9 53.4 51.8 51.4 52.9 55.9 62.7 66.6 59.8 58.7 56.6 59.3 60.0 53.1 68.9 78.1 83.2 Class4 66.6 70.1 65.5 61.0 66.1 63.3 62.1 76.2 92.6 77.9 75.1 73.4 74.5 75.1 74.0 75.1 87.0 96.0 Class5 90.3 90.3 89.8 84.1 91.3 88.9 81.0 93.8 95.9 90.3 91.5 94.3 90.5 92.4 92.0 87.9 95.2 95.9 Class6 91.6 89.0 92.4 85.5 88.1 90.0 89.7 92.5 98.5 91.9 91.2 90.0 88.6 91.9 90.7 92.8 97.4 99.4 Class7 92.3 84.6 92.3 84.6 92.3 92.3 61.5 84.6 100 92.3 92.3 92.3 92.3 92.3 92.3 84.6 84.6 100 Class8 88.7 87.3 84.9 87.0 86.6 84.9 92.1 93.5 94.7 95.2 94.7 88.0 89.2 95.0 95.5 96.6 98.3 99.7 Class9 80 80.0 80.0 100 80.0 60.0 100 100 100 100 100 100 100 100 100 100 100 100 Class10 64.4 63.2 60.0 58.9 62.7 62.6 65.4 76.6 70.1 67.5 68.9 61.4 72.0 65.1 63.7 75.5 80.0 83.0 Class11 47.2 46.9 46.7 46.6 47.4 46.2 47.7 55.7 56.2 55.7 55.4 54.2 53.6 52.9 56.2 62.5 64.1 72.5 Class12 48.9 46.9 47.8 39.7 51.2 46.5 53.1 59.6 73.3 55.9 52.5 60.0 55.7 61.2 60.6 69.6 80.4 81.6 Class13 97.9 95.9 96.6 97.2 96.6 95.2 96.5 99.3 98.6 98.6 98.6 97.9 98.6 97.9 98.6 96.5 99.3 98.6 Class14 75.4 75.7 73.8 84.4 74.9 76.2 77.5 85.6 86.5 76.5 76.7 80.7 88.2 81.8 78.7 84.4 93.2 89.8 Class15 48.7 46.9 52.8 41.7 47.8 48.2 49.6 74.5 87.1 64.7 58.6 58.9 65.3 69.6 62.6 74.8 86.8 86.5 Class16 85.9 85.9 85.9 84.6 88.5 84.6 85.9 85.9 90.1 62.8 73.1 89.7 83.3 88.5 89.7 84.6 85.9 98.7 AA（%） 70.2 68.7 69.6 67.8 70.8 67.6 70.4 79.0 84.3 73.5 74.2 76.0 76.4 79.1 76.5 80.9 85.6 91.1 OA（%） 60.9 60.1 60.2 58.4 60.6 59.7 61.5 70.8 73.2 66.6 66.2 67.1 67.0 68.6 66.9 73.7 79.1 84.7 KA（%） 56.0 55.1 55.2 53.5 55.6 54.7 56.7 67.1 69.8 62.3 61.9 62.8 62.7 64.5 62.6 69.9 77.6 82.6
Table 3. Running time of different algorithms (s)
View tableView in ArticleTable 3. Running time of different algorithms (s)
Dataset PCA LPP LDMGI SPA MSME LESR LMSDL LDGSPP PaviaU 0.02 0.12 2.50 2.24 3.39 46.55 57.47 81.01 Indian Pines 0.03 0.23 2.92 9.62 10.72 101.11 143.38 168.23
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Minghua ZHANG, Hongling LUO, Wei SONG, Dongmei HUANG, Qi HE, Cheng SU. Feature Extraction of Hyperspectral Image Based on Sparse Representation and Learning Graph Regularity[J]. Acta Photonica Sinica, 2021, 50(4): 241
Paper Information
Category: Image Processing
Received: Oct. 16, 2020
Accepted: Feb. 1, 2021
Published Online: May. 11, 2021
The Author Email: SONG Wei (wsong@shou.edu.cn)
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