Xuan Hu, Qikai Lu. Hyperspectral Image Classification Algorithm Based on Saliency Profile[J]. Acta Optica Sinica, 2020, 40(16): 1611001
- Acta Optica Sinica
- Vol. 40, Issue 16, 1611001 (2020)
Fig. 1. Flowchart of classification algorithm
Fig. 2. Tree structure of image. (a) Original image; (b) min-tree; (c) max-tree; (d) tree of shape
Fig. 3. Calculation process of node attributes. (a) Original image; (b) remove node B; (c) tree of shape before removing node B; (d) tree of shape after removing node B
Fig. 4. Real remote sensing image. (a) Sample images of Aν; (b)(c) change curves of
Fig. 5. Hyperspectral images of Indian Pines dataset. (a) False color composite image; (b) survey results of feature types
Fig. 6. Hyperspectral images of Pavia university dataset. (a) False color composite image; (b) survey results of feature types
Fig. 7. Classification results of different algorithms on Indian Pines dataset. (a) SVM; (b) EMP; (c) EMAP; (d) EEP; (e) SC-MK; (f) ESP
Fig. 8. Classification results of different algorithms on Pavia university dataset. (a) SVM; (b) EMP; (c) EMAP; (d) EEP; (e) SC-MK; (f) ESP
Fig. 9. SP of different feature images on Pavia university dataset. (a) Original image; (b) SP0; (c) SP1; (d) SP2; (e) SP3; (f) SP4; (g) SP5; (h) SP6; (i) SP7; (j) SP8; (k) SP9; (l) SP10
Fig. 10. Relationship curves between hν and OA
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Table 1. Number of samples in Indian Pines dataset
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Table 2. Number of samples in Pavia university dataset
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Table 3. Classification accuracy of Indian Pines dataset
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Table 4. Classification accuracy of Pavia university dataset
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Table 5. Comparison of number of scale shapes of different feature images
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