**5. Conclusion**

Hyperspectral imagery consists of hundreds of contiguous spectral bands, but only a subset of well-chosen bands is generally sufficient for a specific classification problem. So it is possible to design superspectral sensors dedicated to specific land cover classification tasks. This chapter presented a spectral optimization strategy to identify the most relevant spectral band subset for such sensor, optimizing both band position and width. Spectral optimization involves a band subset relevance score as well as a method to optimize it.

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This study first focused on the definition of this relevance score. Several filter, wrapper and embedded scores compatible with generic optimization heuristics were compared, and both their classification performance and selection stability were considered for band selection problem. At the end, most of them brought good results. Jeffries-Matusita distance score tended to lead to slightly better quantitative classification results than the best wrapper scores but also being less stable. Wrapper scores taking into account classification confidence performed better than classic wrapper scores expressed as a simple classification "hard label" error rate. For instance, a random forest confidence-based score was identified as one of the best criteria, considering both quantitative and qualitative analyses. As an intermediate result of this FS criteria comparison, a method to create band importance profiles according to the different criteria was proposed providing visual hints about the relevance of the different parts of the spectrum. Then the study focused on the optimization of bandwidth, which is important in a spectral sensor design context, as having wider bands is a way to limit signal noise while having too wide bands can also lead to a loss a useful information. A strategy consisting in building a hierarchy of groups of adjacent bands before applying band selection at the different levels of this hierarchy using an adaptation of an incremental algorithm for this problem. This band grouping strategy enabled to limit the problem's combinatory while considering relevant band subsets composed of spectral bands with different spectral widths. It was also a way to consider several possible solutions and evaluate their impact.

To conclude, algorithms proposed in this study were applied to design a sensor dedicated to classify urban materials [36, 72].
