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Hyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 5535116 Titre : Hyperspectral unmixing using transformer network Type de document : Article/Communication Auteurs : Preetam Ghosh, Auteur ; Swalpa Kumar Roy, Auteur ; Bikram Koirala, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5535116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image hyperspectrale Résumé : (auteur) Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU . Numéro de notice : A2022-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3196057 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3196057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101518 [article]

A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements / Bikram Koirala in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7393 - 7405 Titre : A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements Type de document : Article/Communication Auteurs : Bikram Koirala, Auteur ; Zohreh Zahiri, Auteur ; Paul Scheunders, Auteur Année de publication : 2020 Article en page(s) : pp 7393 - 7405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] biochimie [Termes IGN] diagnostic foliaire [Termes IGN] feuille (végétation) [Termes IGN] indice de végétation [Termes IGN] méthode fondée sur le noyau [Termes IGN] processus gaussien [Termes IGN] réflectance spectrale [Termes IGN] régression [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400–2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set. Numéro de notice : A2020-589 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2982263 Date de publication en ligne : 02/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2982263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95919 [article]