Hyperspectral Image Classification Algorithm Based on Saliency Profile

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

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Fig. 4. Real remote sensing image. (a) Sample images of A_ν; (b)(c) change curves of $A_{\nabla}$ and A_ν from leaf node to root node; (d)(e) significant areas corresponding to two significant maximum points in Fig. (c)

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Fig. 5. Hyperspectral images of Indian Pines dataset. (a) False color composite image; (b) survey results of feature types

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Fig. 6. Hyperspectral images of Pavia university dataset. (a) False color composite image; (b) survey results of feature types

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Fig. 7. Classification results of different algorithms on Indian Pines dataset. (a) SVM; (b) EMP; (c) EMAP; (d) EEP; (e) SC-MK; (f) ESP

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Fig. 8. Classification results of different algorithms on Pavia university dataset. (a) SVM; (b) EMP; (c) EMAP; (d) EEP; (e) SC-MK; (f) ESP

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Fig. 9. SP of different feature images on Pavia university dataset. (a) Original image; (b) S_P0; (c) S_P1; (d) S_P2; (e) S_P3; (f) S_P4; (g) S_P5; (h) S_P6; (i) S_P7; (j) S_P8; (k) S_P9; (l) S_P10

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Fig. 10. Relationship curves between h_ν and OA

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Table 1. Number of samples in Indian Pines dataset

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Table 1. Number of samples in Indian Pines dataset

No.	Name	Number of samples
1	Corn-notill	1428
2	Corn-mintill	830
3	Grass-pasture	483
4	Grass-tree	730
5	Hay-windrow	478
6	Soybean-notill	972
7	Soybean-mintill	2455
8	Soybean-clean	593
9	Wood	1265
10	Building-grass-tree-drive	386
Total		9620

Table 2. Number of samples in Pavia university dataset
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Table 2. Number of samples in Pavia university dataset
No. Name Number of samples
1 Asphalt 6631
2 Meadow 18649
3 Gravel 2099
4 Tree 3064
5 Painted metal sheet 1345
6 Bare soil 5029
7 Bitumen 1330
8 Self-blocking brick 3682
9 Shadow 947
Total 42776

Table 3. Classification accuracy of Indian Pines dataset

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Table 3. Classification accuracy of Indian Pines dataset

Class	SVM	EMP	EMAP	EEP	SC-MK	ESP
1	66.99	70.29	76.79	79.30	81.00	92.54
2	67.59	70.92	78.60	79.28	87.78	94.88
3	91.48	90.81	91.34	92.24	95.61	97.71
4	93.71	94.81	96.65	97.40	97.32	99.94
5	99.44	99.39	99.65	99.51	99.98	100.00
6	74.32	69.92	82.66	84.39	85.80	94.08
7	55.89	60.64	72.67	74.02	77.09	91.86
8	72.69	67.66	74.99	79.96	88.03	93.08
9	84.32	85.88	92.91	93.79	94.04	99.10
10	73.27	79.58	92.77	94.88	94.67	98.87
OA /%	72.41	74.20	82.33	83.88	86.50	95.00
AA /%	77.97	78.99	85.90	87.48	90.13	96.20
K	68.38	70.40	79.63	81.42	84.40	94.19

Table 4. Classification accuracy of Pavia university dataset

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Table 4. Classification accuracy of Pavia university dataset

Class	SVM	EMP	EMAP	EEP	SC-MK	ESP
1	93.76	98.17	93.46	94.48	95.37	98.27
2	94.48	98.47	90.88	95.79	95.62	97.65
3	67.72	73.85	89.47	97.40	97.76	96.10
4	81.87	96.86	97.07	98.79	96.34	86.84
5	95.78	98.89	99.20	99.52	99.96	99.82
6	62.66	85.03	95.68	96.65	97.78	99.99
7	60.36	94.66	97.38	97.62	99.95	100.00
8	81.19	92.51	85.97	97.16	94.84	97.65
9	99.53	99.69	100.00	98.13	99.99	98.22
OA /%	84.24	94.43	92.44	96.76	96.28	97.33
AA /%	79.72	92.76	94.35	97.53	97.51	97.17
K	82.52	93.01	90.12	95.68	95.11	96.45

Table 5. Comparison of number of scale shapes of different feature images
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Table 5. Comparison of number of scale shapes of different feature images
Feature map Number of scale shapes
Original image 53653
S_P0 2259
S_P1 1072
S_P2 645
S_P3 416
S_P4 242
S_P5 170
S_P6 120
S_P7 103
S_P8 85
S_P9 60
S_P10 41