A split-and-merge approach for hyperspectral band selection

Shaheera Rashwan (1) , Nicolas Dobigeon (2, 3)

1 Informatics Research Institute - IRI (Alexandrie, Egypte)
2 IRIT-SC - Signal et Communications
3 Toulouse INP - Institut National Polytechnique (Toulouse)

The problem of band selection (BS) is of great importance to handle the curse of dimensionality for hyperspectral image (HSI) applications (e.g., classification). This letter proposes an unsupervised BS approach based on a split-and-merge concept. This new approach provides relevant spectral sub-bands by splitting the adjacent bands without violating the physical meaning of the spectral data. Next, it merges highly correlated bands and sub-bands to reduce the dimensionality of the HSI. Experiments on three public data sets and comparison with state-of-the-art approaches show the efficiency of the proposed approach.

Mots clés: Band selection (BS); Data reduction; Hyperspectral image (HSI)

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Shaheera Rashwan, Nicolas Dobigeon. A split-and-merge approach for hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (8), pp.1378-1382. ⟨10.1109/LGRS.2017.2713462⟩. ⟨hal-01887849⟩