Unsupervised processing of hyperspectral images

Benoît Jaillais (1) , Karima Meghar (1) , Ludivine Nus (2) , Sebastian Miron (2) , David Brie (2) , Saïd Moussaoui (3)

1 StatSC - Statistique, Sensométrie et Chimiométrie
2 CRAN - Centre de Recherche en Automatique de Nancy
3 LS2N - Laboratoire des Sciences du Numérique de Nantes

This work is a part of the CNRS project “ALOHA: Analyse en Ligne de dOnnées Hyperspectrales pour l’industrie Agroalimentaire” and of the ANR-OPTIFIN (Agence Nationale de la Recherche-OPTImisation des FINitions). The aim of these projects is to develop analytical tools adapted to the high throughput online analysis of samples by acquisition and processing of hyperspectral images. One output of the ALOHA and ANR OPTIFIN projects consists in the development of sequential algorithms for the deconvolution and on-the-fly unmixing of hyperspectral data. The main goal is to be able to predict and classify the quality of wood pieces renderings.

Mots clés: k-means; on-line NMF; Near infrared hyperspectral imaging (NIR-HIS); Wood sorting

Domaines: Traitement du signal et de l'image [eess.SP]

Benoît Jaillais, Karima Meghar, Ludivine Nus, Sebastian Miron, David Brie, et al.. Unsupervised processing of hyperspectral images. CHIMIOMETRIE XIX, Jan 2018, Paris France. ⟨hal-01774088⟩