The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
450 
< 
o 
100 
99 
98 
97 
96 
95 
94 
93 
92 
| - QDC-Validation Data] 
2 4 6 8 10 12 14 16 18 20 
Number of bands 
Figure 5 OA of experiment 1 for validation data 
3.2.1 Accuracy assessment 
To test the accuracy of the proposed method, the disjoint test 
data set were used. We note that in the PSBS method only 40 
percent of the training data set was used to train QDC, but once 
the features were defined, we used the whole training data set to 
train QDC. The classification results are shown in Figure 6 in 
terms of OA for the independent test data set. PSBS was 
conducted independently by K-means for each feature size. It 
can be observed two consecutive feature sizes (number of bands) 
will not necessarily result in similar classification accuracies. 
However, the trend of OA curves shows the curse of 
dimensionality: lower performances for larger feature sizes. 
3.3 Comparison of PSBS with other BS methods 
The performance of the PSBS approaches have been 
compared with Sequential Floating Forward Selection 
(SFFS), Sequential Floating Backward Selection (SFBS) 
as feature selection methods. SFFS and SFBS were 
performed based on the entire training data. OA, AA, and 
the classification accuracies of classes corresponding to 
maximum OA of three FS methods are shown in Table II. 
Class Name 
PSBS- 
Validation 
Data 
SFFS 
PSBS 
SFBS 
Hay-windrowed 
100 
100 
99.59 
99.59 
Com-min 
100 
80.81 
80.81 
80.81 
Soybean-clean 
100 
45.18 
45.19 
40 
Grass/Trees 
100 
100 
100 
100 
Com-min 
100 
96.97 
95.96 
97.98 
Soybeans-no 
till 
100 
91.66 
96.06 
93.294 
Woods 
100 
100 
97.66 
97.51 
Com-no till 
83.33 
48.15 
59. 6 
55.55 1 
Soybeans-min 
97.29 
76.26 
70.737 
64.55 
OA 
98.38 
84.75 
84.79 
82.38 1 
|aa 
97.85 
82.12 
82.84 
81.031 j 
Table II Classification accuracy of Three BS methods for 
disjoint test data. 
OA of the supervised PSBS approach with OA of the SFFS and 
SFBS methods are shown in Figure 8. As illustrated, supervised 
PSBS achieved higher OAs with respect to SFBS and SFFS for 
almost all feature sizes, thus suggesting improved class 
accuracies. The best result in terms of OA was provided by the 
PSBS; however, the maximum overall accuracies given by 
SFFS and the PSBS approach are very close together (i.e., 
84.75% for SFFS with 4 bands; 84.79% for supervised PSBS 
with ten bands). Moreover, the maximum OA of PSBS for 
small training sample size in comparison to SFFS is remarkable. 
As expected, in the three BS methods the classification 
accuracy tends to increase with increasing feature size until 
maximum value is reached, but almost monotonically decreases 
for larger feature values due to the curse of dimensionality. 
Different classification accuracies can be observed by 
comparing class to class in Table II. Particularly, the corn-no 
till, soybeans-no till, and soybeans-min classes yielded different 
accuracies for the PSBS, SFFS, and SFBS. This result may be 
due to the use of different search strategies and PSBS as they 
explore the bans subsets and prototype space in different ways. 
Furthermore, as we pointed out in the supervised PSBS, the 
training data set was split into two parts: one for the prototype 
space generation and one for the resulting band subset 
validation. However, the experimental results show that limited 
training data for generating the prototype space is adequate to 
achieve comparable results with SFFS. In contrast to the SFFS 
and SFBS methods, which are based on the estimation of 
scattering matrices in high dimension, the PSBS methods are 
based only on the first statistic parameters. As a result, and as 
expected, PSBS shows comparable performance with limited 
training data set size. 
Figure 6. OA of three BS methods for disjoint test data 
4. CONCLUSION 
In this paper, an innovative band selection method called PSBS 
is proposed with two approaches for dimensionality reduction 
of hyperspectral data based only on class spectra. 
Compared to the traditional BS methods, in PSBS, search 
strategies are substituted by K-means clustering to find relevant 
bands in order to determine representative band of each cluster. 
Moreover, instead of optimising separability criteria, overall 
classification accuracy of a validation data set is used to decide 
which disjoint optical regions yield maximum accuracy. From 
the pattern recognition viewpoint, compared to conventional BS 
which possibly examine all set of bands even neighbour bands, 
in PSBS the relevant bands are distinguished as a group of 
highly correlated bands and the highly correlated bands are 
ignore to contribute in the BS process. 
Supervised PSBS is assessed and compared with SFFS and 
SFBS in terms of classification accuracy. Compared to the