*3.4.3 Conclusion*

FS score comparison. Some wrapper, embedded and filter FS scores were tested and evaluated on several data sets:

	- Filter separability scores tend to lead to slightly better classification results than wrapper scores. Especially *jm* often appears as the best FS score according to quantitative analysis. However, considering band importance profiles, it tends to lead to less regular profiles and thus to less stable solutions than some wrapper scores. Besides they appear to be sensitive to an atmospheric correction artifact for Pavia data set.

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

◦ Confidence-based wrapper scores taking into account classification confidence (*rf.conf* or *svm.lin.conf*) perform better than classic wrapper scores expressed as a simple classification "hard label" error rate. This trend could be observed both in quantitative (classification performance) and qualitative (band importance profiles) analyses. Indeed, taking into account classification confidence tends to regularize feature importances and provide more stable feature subsets.

At the end, the most interesting FS scores are *rf.conf* for wrappers and *jm* for filters, since they lead to the best quantitative results. *rf.conf* seems to provide more stable results than *jm*, considering its more regularized band importance profile. Besides it is more robust to some artefacts (e.g. atmospheric correction artefact for Pavia). However, even though computing times were not discussed in this study, it must be added that FS selection using filter separability measures (such as *jm*) is faster than using wrapper scores such as *rf.conf*.

Thematic comments. Conclusions about interesting spectrum parts can be drawn using the importance profiles provided by the different FS criteria:

