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Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3909 - 3921 Titre : Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing Type de document : Article/Communication Auteurs : Wei He, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 3909 - 3921 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] pondération [Termes IGN] variation totale Résumé : (Auteur) Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their corresponding fractional abundances, is an important task for hyperspectral analysis. Recently, nonnegative matrix factorization (NMF) and its extensions have been widely used in HU. Unfortunately, most of the NMF-based methods can easily lead to an unsuitable solution, due to the nonconvexity of the NMF model and the influence of noise. To overcome this limitation, we make the best use of the structure of the abundance maps, and propose a new blind HU method named total variation regularized reweighted sparse NMF (TV-RSNMF). First, the abundance matrix is assumed to be sparse, and a weighted sparse regularizer is incorporated into the NMF model. The weights of the weighted sparse regularizer are adaptively updated related to the abundance matrix. Second, the abundance map corresponding to a single fixed endmember should be piecewise smooth. Therefore, the TV regularizer is adopted to capture the piecewise smooth structure of each abundance map. In our multiplicative iterative solution to the proposed TV-RSNMF model, the TV regularizer can be regarded as an abundance map denoising procedure, which improves the robustness of TV-RSNMF to noise. A number of experiments were conducted in both simulated and real-data conditions to illustrate the advantage of the proposed TV-RSNMF method for blind HU. Numéro de notice : A2017-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2683719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86417 [article]

Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data / Guang Zheng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1475 - 1487 Titre : Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data Type de document : Article/Communication Auteurs : Guang Zheng, Auteur ; Lixia Ma, Auteur ; Wei He, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1475 - 1487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bois sur pied [Termes IGN] classification automatique [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] indice foliaire [Termes IGN] Leaf Area Index [Termes IGN] photosynthèse [Termes IGN] régression linéaire [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) The spatial distribution of the photosynthetic components of a forest canopy plays a key role in ecological related processes such as gas exchange, photosynthesis, and evapotranspiration through affecting radiation regimes of the forest canopy. However, quantitative evaluation of woody materials' contribution to effective leaf area index (LAIe) using 3-D terrestrial laser scanning (TLS) is a challenging work. In this paper, we first identified the differences between directional gap fraction (DGF) and angular gap fraction (AGF) and then developed a local geometric feature-based approach to automatically classify a TLS forest point cloud data (PCD) into three different classes, including nonphotosynthetic canopy components (i.e., stem and branch points), photosynthetic canopy components (i.e., leaf and grass points), and bare ground. In addition, we proposed a new approach named “radial hemispherical point cloud slicing” algorithm to investigate the 3-D spatial distribution of foliage elements and retrieve LAIe from a given forest PCD. Our results showed that nonphotosynthetic canopy components contributed from 19% to 54% to LAIe depending on various forest densities. Moreover, TLS-based LAIe estimates can explain 74.27% variations of digital-hemispherical-photography-based LAIe values with a linear regression statistical model. This paper provides a theoretical foundation for LAI estimation based on the PCD generated using the TLS system and facilitates the application of TLS on retrieving 3-D forest canopy structural biophysical parameters. Numéro de notice : A2016-132 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2481492 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80019 [article]

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Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 178 - 188 Titre : Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration Type de document : Article/Communication Auteurs : Wei He, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur ; Huanfeng Shen, Auteur Année de publication : 2016 Article en page(s) : pp 178 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] factorisation [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] restauration d'image Résumé : (Auteur) In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L1-norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is detected by the L1-norm regularization. To tradeoff the nuclear norm and TV regularization and to further remove the Gaussian noise of the HSI, we also restrict the rank of the clean image to be no larger than the number of endmembers. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed LRTV method for HSI restoration. Numéro de notice : A2016-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2452812 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79834 [article]

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