Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification
Table 1
Training set for Indian Pines.
ID
Class name
1
Alfalfa (46/16, 34.78%)*
2
Corn-no-till (1428/121, 8.47%)
3
Corn-min-till (830/72, 8.67%)
4
Corn (237/31, 13.08%)
5
Grass-pasture (483/48, 9.94%)
6
Grass-trees (730/75, 10.27%)
7
Grass-pasture-mowed (28/16, 57.14%)
8
Hay-windrowed (478/48, 10.04%)
9
Oats (20/15, 75.00%)
10
Soybean-no-till (972/80, 8.23%)
11
Soybean-min-till (2455/234, 9.53%)
12
Soybean-clean (593/52, 8.77%)
13
Wheat (205/34, 16.59%)
14
Woods (1265/114, 9.01%)
15
Buildings-grass-trees-drives (386/53, 13.73%)
16
Stone-steel-towers (93/20, 21.51%)
Total
10249/1029
Numerical value in each row refers to number of total samples, number of training samples, and percentage of training samples in each class, respectively.