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Deep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 5527214 Titre : Deep generative model for spatial–spectral unmixing with multiple endmember priors Type de document : Article/Communication Auteurs : Shuaikai Shi, Auteur ; Lijun Zhang, Auteur ; Yoann Altmann, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5527214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image hyperspectrale [Termes IGN] réseau neuronal de graphes Résumé : (auteur) Spectral unmixing is an effective tool to mine information at the subpixel level from complex hyperspectral images. To consider the spatially correlated materials distributions in the scene, many algorithms unmix the data in a spatial–spectral fashion; however, existing models are usually unable to model spectral variability simultaneously. In this article, we present a variational autoencoder-based deep generative model for spatial–spectral unmixing (DGMSSU) with endmember variability, by linking the generated endmembers to the probability distributions of endmember bundles extracted from the hyperspectral imagery via discriminators. Besides the convolutional autoencoder-like architecture that can only model the spatial information within the regular patch inputs, DGMSSU is able to alternatively choose graph convolutional networks or self-attention mechanism modules to handle the irregular but more flexible data—superpixel. Experimental results on a simulated dataset, as well as two well-known real hyperspectral images, show the superiority of our proposed approach in comparison with other state-of-the-art spatial–spectral unmixing methods. Compared to the conventional unmixing methods that consider the endmember variability, our proposed model generates more accurate endmembers on each subimage by the adversarial training process. The codes of this work will be available at https://github.com/shuaikaishi/DGMSSU for the sake of reproducibility. Numéro de notice : A2022-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3168712 Date de publication en ligne : 18/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3168712 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100645 [article]