On-line blind unmixing for hyperspectral pushbroom imaging systems

International audienceIn this paper, the on-line hyperspectral image blind unmixing is addressed. Inspired by the Incremental Non-negative Matrix Factorization (INMF) method, we propose an on-line NMF which is adapted to the acquisition scheme of a pushbroom imager. Because of the non-uniqueness of the NMF model, a minimum volume constraint on the endmembers is added allowing to reduce the set of admissible solutions. This results in a stable algorithm yielding results similar to those of standard off-line NMF methods, but drastically reducing the computation time. The algorithm is applied to wood hyperspectral images showing that such a technique is effective for the on-line prediction of wood piece rendering after finishing. Index Terms— Hyperspectral imaging, Pushbroom imager, On-line Non-negative Matrix Factorization, Minimum volume constraint

Démélange d'images hyperspectrales à l'aide de la NMF en-ligne avec contrainte de dispersion minimale

National audienceWe propose a new method for on-line hyperspectral images unmixing based on an ADMM (Alternating Direction Method of Multipliers) approach, particularly well-adapted to pushbroom imaging systems. The proposed algorithm presents a faster convergence and a lower computational complexity compared to the algorithms based on multiplicative update rules. Due to the general ill-posed nature of the unmixing problem, we impose a minimal endmembers dispersion constraint; this constraint can be interpreted as a convex relaxation of the minimal volume constraint. Real data tests illustrate the good performance of the proposed method compared to the state of the art.Nous proposons une nouvelle méthode de démélange en-ligne d'images hyperspectrales fondée sur une approche de type ADMM (Alternating Direction Method of Multipliers), particulièrement bien adaptée aux systèmes d'imagerie pushbroom. L'algorithme proposé présente une convergence plus rapide et une complexité de calcul plus faible par rapport aux algorithmes fondés sur des règles de mise à jour multiplicatives. En raison du caractère généralement mal posé du problème de démélange, nous intégrons dans la méthode une contrainte de dispersion minimale des endmembers ; cette contrainte peut être interprétée comme une relaxation convexe de la contrainte de volume minimal. Des tests sur des données réelles permettent d'illustrer les bonnes performances de la méthode proposée comparées à l'état de l'art

Estimation of the regularization parameter of an on-line NMF with minimum volume constraint

International audienceIn this paper, the estimation of the regularization parameter of the on-line Non-negative Matrix Factorization (NMF) with minimum volume constraint on sources is addressed. Adding a volume constraint in the model is important to ensure uniqueness of the solution and good data representation. However, the effectiveness of this approach is hampered by the optimal determination of the strength of minimum volume term. To solve this problem, we formulate it as a bi-objective optimization problem and three Minimum Distance Criterion (MDC) strategies are proposed and evaluated. The three strategies yield similar results but one of them in particular yields an interesting tradeoff between accuracy and computation time

Unsupervised processing of hyperspectral images

National audienceThis 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