*4.3.2.2 Results*

Obtained results on Pavia scene for the band merging criterion 'spectra piecewise approximation error' are presented in **Figure 17**: five merged bands were selected at each level of the hierarchy, starting from an initial solution obtained at the bottom level of the hierarchy.

As for previous experiments, obtained results were evaluated both for Pavia (5 selected bands) and Indian Pines (10 selected bands) data sets. Kappa reached for rbf SVM classification for merged band subsets selected at the different levels of the hierarchy (built for band merging criterion 'spectra piece-wise approximation

#### **Figure 17.**

*Pavia data set: Selected bands at the different levels of the hierarchy using the proposed hierarchy aware algorithm for a hierarchy of merged bands obtained using spectra piece-wise approximation error band merging criteria.*

*Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification… DOI: http://dx.doi.org/10.5772/intechopen.88507*

error') can be seen both for the greedy FS algorithm and for the hierarchy aware one in **Figure 18**: obtained results remain very close, whatever the optimization algorithm.

Both algorithms lead to equivalent results considering classification performance (see **Table 4**), while the proposed hierarchy aware algorithm is really faster.

#### **Figure 18.**

*Kappa (in %) reached for rbf SVM classification for merged band subsets selected at the different levels of the hierarchy (built for band merging criterion 'spectra piece-wise approximation error') for Pavia and Indian Pines data sets, using the hierarchy aware band selection algorithm.*


#### **Table 4.**

*Computing times and best kappa coefficients reached on Pavia (for a 5-band subset) and Indian Pines (for a 10-band subset) data sets for band merging criterion 'spectra piece-wise approximation error'.*
