Testing the suitability of v-Support Vector Machine for hyperspectral image classification

Figures & data

Figure 1. (a) False colour image of Indian pines (b) ground truth labels of Indian pines (c) false colour of University of Pavia (d) ground truth labels of University of Pavia.

Table 1. Training and test sets for Indian Pines.

Class	Name	Training set	Test set
1	Corn-mintill	84	754
2	Corn-notill	139	1282
3	Soybean-mintill	247	2211
4	Woods	130	1142
5	Grass-trees	74	657
6	Soybean-clean	57	531
7	Grass-pasture	48	436
8	Soybean-notill	95	873
9	Buildings-grass-trees-drives	42	351
10	Stone-steel-towers	10	87
11	Alfalfa	6	41
12	Wheat	8	44
13	Grass-pasture-mowed	8	26
14	Corn	25	211
15	Oats	7	17
16	Hay-windrowned	42	429
Total		1022	9092

Table 2. Training and test sets for University of Pavia.

Class	Name	Training set	Test set
1	Meadows	190	18449
2	Asphalt	66	6562
3	Gravel	21	2079
4	Trees	32	3031
5	Self-blocking bricks	42	3645
6	Painted metal sheets	15	1332
7	Bare soil	49	4980
8	Bitumen	15	1320
9	Shadows	11	940
Total		441	42338

Figure 2. Selection of optimal $\mathit{v}$ value at $\mathit{\gamma }$ thresholds for Indian Pines.

Figure 3. Selection of optimal $\mathit{v}$ value at $\mathit{\gamma }$ thresholds for University of Pavia.

Figure 4. Correlation plots at $\mathit{\gamma }$ thresholds for Indian Pines.

Figure 5. Correlation plots at $\mathit{\gamma }$ thresholds for University of Pavia.

Figure 6. Indian Pines raw classification results for v-SVM, SVM, GMM, KNN, and KM.

Figure 7. Indian pines overlaid classification results for v-SVM, SVM, GMM, KNN, and KM.

Figure 8. University of Pavia raw classification results for v-SVM, SVM, GMM, KNN, and KM.

Figure 9. University of Pavia overlaid classification results for v-SVM, SVM, GMM, KNN, and KM.

Table 3. Classification accuracy for Indian pines.

		Models
Class	Name	v-SVM	SVM	GMM	KNN	KM
1	Corn-mintill	75.95	74.82	92.68	92.06	22.87
2	Corn-notill	72.02	71.87	87.73	88.50	20.10
3	Soybean-mintill	76.24	73.09	90.42	94.74	28.84
4	Woods	70.65	69.64	91.08	93.18	19.52
5	Grass-trees	74.86	70.13	90.52	92.85	27.06
6	Soybean-clean	75.75	72.74	91.09	93.11	24.69
7	Grass-pasture	80.34	79.86	90.81	93.59	21.74
8	Soybean-notill	81.78	75.97	90.68	89.54	23.35
9	Buildings-grass-trees-drives	78.45	72.43	90.04	90.81	19.01
10	Stone-steel-towers	87.78	84.99	93.01	94.04	25.84
11	Alfalfa	82.03	81.42	88.43	89.75	22.27
12	Wheat	71.87	68.84	84.56	85.00	18.53
13	Grass-pasture-mowed	77.46	74.75	86.85	86.47	21.98
14	Corn	86.46	84.89	91.17	91.22	25.86
15	Oats	60.75	55.32	75.23	75.96	20.75
16	Hay-windrowned	75.21	70.77	84.10	83.90	19.64

Table 4. Classification accuracy for University of Pavia.

		Models
Class	Name	v-SVM	SVM	GMM	KNN	KM
1	Meadows	74.05	71.13	90.97	90.11	23.54
2	Asphalt	80.72	77.89	91.13	92.72	20.60
3	Gravel	74.03	70.28	86.96	87.65	24.53
4	Trees	76.98	72.56	84.73	86.54	22.45
5	Self-blocking bricks	78.99	75.03	93.56	94.83	23.41
6	Painted metal sheets	69.01	67.66	92.84	93.75	24.38
7	Bare soil	72.64	70.88	88.43	89.97	23.06
8	Bitumen	79.75	75.40	90.52	91.69	25.67
9	Shadows	80.11	78.87	90.19	91.53	22.01

Table 5. Summary of classification accuracies for Indian pines and University of Pavia.

Type	v-SVM	SVM	GMM	KNN	KM
Indian Pines	76.73	73.85	88.65	89.67	22.63
University of Pavia	76.25	73.30	89.93	90.98	23.29