Non-linear unmixing methods for hyperspectral imaging
type: Distribution
access URL: https://theses.hal.science/tel-00950388
format: HTML
issued: 2026-05-06T07:03:55.466876
modified: 2026-05-06T07:03:55.435671

test_moissonnage_selune
type: Agent

Non-linear unmixing methods for hyperspectral imaging
type: Dataset
contact point: Organization
  fn: CCSD
keyword: spatial-regularization
keyword: non-linear-unmixing
keyword: demelange-non-lineaire
keyword: preimage
keyword: theses
keyword: apprentissage-de-variete
keyword: infoeu-reposemanticsdoctoralthesis
keyword: imagerie-hyperspectrale
keyword: regularisation-spatiale
keyword: manifold-learning
keyword: hyperspectral-image
keyword: sduothersciences-of-the-universe-physicsother
landing page: https://theses.hal.science/tel-00950388
description: In this thesis , we present several aspects of hyperspectral imaging technology , while focusing on the problem of non- linear unmixing . We have proposed three solutions for this task. The first one is integrating the advantages of manifold learning in classical unmixing methods to design their nonlinear versions . Results with data generated on a well-known manifold- the " Swissroll " - seem promising. The methods work much better with the increase in non- linearity compared with their linear version. However, the absence of constraint of non- negativity in these methods remains an open question for improvements . The second proposal is using the pre-image method for estimating an inverse transformation of the data form pixel space to abundance of space . The adoption of spatial information as " total variation " is also introduced to make the algorithm more robust to noise . However, the problem of obtaining ground truth data required for learning step limits the application of such algorithms.
identifier: NNT: 2013NICE4113
issued: 2026-05-06T07:03:55.463635
language: fr
modified: 2026-05-06T07:03:55.463641