IEEE Geoscience and Remote Sensing Magazine - March 2018 - 26
TaBLE 7. ThE SVm cLaSS-SPEcIFIc accUracY (%) aND Oa OF DIFFErENT TEchNIQUES
FOr ThE UNIVErSITY OF PaVIa DaTa.
LDa
LFDa
SGDa
cGDa
KDa
KLFDa
KSGDa
KcGDa
1
91.53
94.66
95.02
95.10
91.94
94.33
93.21
93.38
2
95.18
96.39
97.50
97.86
96.40
96.57
97.77
97.36
3
63.54
68.98
74.99
71.21
60.33
67.37
79.49
76.06
4
88,68
95.14
94.22
92.91
87.94
92.09
90.81
91.59
5
99.60
99.68
99.51
99.60
97.66
98.81
99.27
99.35
6
76.90
88.72
89.80
89.28
81.95
89.73
89.97
89.67
7
64.30
75.74
80.15
72.30
75.98
82.19
81.94
81.05
8
83.73
86.01
91.44
86.15
87.75
87.78
91.61
93.09
9
99.66
99.77
99.89
99.89
99.31
99.43
100
99.77
oa
88.74
92.43
93.92
93.06
90.36
92.59
93.83
93.62
aa
87.74
91.26
93.38
91.96
90.27
91.49
93.21
92.60
Kappa
0.85
0.90
0.92
0.91
0.87
0.90
0.92
0.92
TaBLE 8. ThE SVm cLaSS-SPEcIFIc accUracY (%) aND Oa OF DIFFErENT TEchNIQUES FOr ThE SaLINaS DaTa.
LDa
LFDa
SGDa
cGDa
KDa
KLFDa
KSGDa
KcGDa
1
99.75
99.90
99.65
99.69
98.95
99.11
98.74
98.53
2
99.79
99.94
99.33
99.75
100
99.75
99.24
99.89
3
99.75
99.79
99.30
99.79
97.71
98.83
98.24
99.09
4
99.64
99.24
99.21
99.40
99.70
99.47
99.70
99.62
5
98.54
98.66
99.07
98.70
97.21
98.82
97.05
97.29
6
99.77
99.36
99.57
99.20
99.60
99.65
99.23
99.63
7
99.80
99.88
99.27
99.85
99.68
99.71
99.50
99.59
8
84.38
88.21
89.78
89.29
91.54
90.61
89.33
89.67
9
98.19
99.63
100
99.49
98.85
99.51
98.05
99.56
10
97.99
96.98
97.99
97.56
94.64
95.66
94.22
95.54
11
98.31
98.13
99.53
98.92
96.75
98.72
94.88
98.03
12
99.74
99.89
100
100
99.45
99.95
99.78
99.84
13
98.69
98.62
98.47
98.05
97.70
98.51
97.13
98.05
14
96.45
94.49
96.07
94.78
93.50
92.42
90.85
91.63
15
60.66
67.86
65.40
67.07
67.44
69.93
63.90
71.63
16
99.39
99.30
99.34
99.30
99.24
99.48
99.01
99.18
oa
90.85
92.65
91.82
92.78
92.81
93.26
92.22
93.15
aa
95.68
96.64
96.38
96.67
96.07
96.74
95.37
96.32
Kappa
0.90
0.92
0.93
0.92
0.92
0.92
0.91
0.92
to construct the affinity matrix, and their performance
is always better than LFDA and LDA. On the other hand,
because the distribution of hyperspectral data is usually
complex, kernel versions of these algorithms are superior to their linear counterparts. Take the Indian Pines
data, for example. The accuracy of KLFDA is 84.75%,
which is around 5% higher than that of LFDA; for both
KSGDA and KCGDA, there is an improvement of approximately 3.5%.
In Figures 7-9, we list classification maps of different
dimensionality-reduction techniques, which are consistent
with the results in Tables 6-8. For example, we observe that
kernel-based methods produce smoother maps than linear
26
methods in classifying the sixth class when using the Indian Pines data.
We also conducted an experiment to show the sensitivity to changes of training-data set sizes. Figures 10-12
illustrate the OA as a function of the ratio of training
samples to the total labeled samples. For the Salinas data,
the training size is changed from 0.01 to 0.05 (note that
0.01 is the ratio of the number of training samples to the
total labeled data). To avoid any bias, we randomly chose
the training samples for each sample-size ratio, and we
repeated the experiment ten times and reported the mean
accuracy. For the Indian Pines data, the ratio range is
0.06-0.1; for the University of Pavia data, it is 0.05-0.09.
ieee Geoscience and remote sensing magazine
march 2018
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