1 Ocak 2019 Konferans bildirisi Açık Erişim

Kahraman, Sevcan; Xu, Yang; Chanussot, Jocelyn; Tangel, Ali

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        "affiliation": "Kocaeli Univ Elect & Telecommun Engn, TR-41100 Kocaeli, Turkey", 
        "name": "Kahraman, Sevcan"
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      {
        "affiliation": "Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China", 
        "name": "Xu, Yang"
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      {
        "affiliation": "Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France", 
        "name": "Chanussot, Jocelyn"
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      {
        "affiliation": "Kocaeli Univ Elect & Telecommun Engn, TR-41100 Kocaeli, Turkey", 
        "name": "Tangel, Ali"
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    "description": "In recent years, there have been many advances in sensor technology which provides more useful information about the observed scene. Two of the newest remote sensing technologies are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. Since pixels in a small spatial neighborhood are more likely to share similar abundances, hypergraph regularization (HG-NMF) can be employed to handle the similarity relevance among the spatial neighborhood pixels. In this paper, we provide a LiDAR data-aided HS unmixing using HG-NMF. The composite usage of all these valuable information can lead to higher accuracy unmixing results. The obtained convex optimization problem is solved by Spectral Unmixing by Split Augmented Lagrangian (SUnSAL-TV) Algorithm. Experiments on synthetic data are conducted. The advantage of HG-NMF regularization is also demonstrated.", 
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      "title": "2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)"
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    "title": "LiDAR DATA-AIDED HYPERGRAPH REGULARIZED MULTI-MODAL UNMIXING"
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