@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00950388v1> a dcat:Dataset ;
    dct: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.
            """ ;
    dct:identifier "NNT: 2013NICE4113" ;
    dct:issued "2026-05-06T07:03:55.463635"^^xsd:dateTime ;
    dct:language "fr" ;
    dct:modified "2026-05-06T07:03:55.463641"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Non-linear unmixing methods for hyperspectral imaging" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00950388v1/resource/946877f8-b090-41be-9c40-799b8f054dd3> ;
    dcat:keyword "apprentissage-de-variete",
        "demelange-non-lineaire",
        "hyperspectral-image",
        "imagerie-hyperspectrale",
        "infoeu-reposemanticsdoctoralthesis",
        "manifold-learning",
        "non-linear-unmixing",
        "preimage",
        "regularisation-spatiale",
        "sduothersciences-of-the-universe-physicsother",
        "spatial-regularization",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00950388> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00950388v1/resource/946877f8-b090-41be-9c40-799b8f054dd3> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-06T07:03:55.466876"^^xsd:dateTime ;
    dct:modified "2026-05-06T07:03:55.435671"^^xsd:dateTime ;
    dct:title "Non-linear unmixing methods for hyperspectral imaging" ;
    dcat:accessURL <https://theses.hal.science/tel-00950388> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00950388> a foaf:Document .