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Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 4 (April 2017) . - pp 307 - 316 Titre : Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints Type de document : Article/Communication Auteurs : L. Chen, Auteur ; Shengbo Chen, Auteur ; Xulin Guo, Auteur Année de publication : 2017 Article en page(s) : pp 307 - 316 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] calcul matriciel [Termes IGN] contrainte spectrale [Termes IGN] factorisation de matrice non-négative [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] processus de hiérarchisation analytique [Termes IGN] programmation par contraintes [Termes IGN] réflectance Résumé : (Auteur) Due to the coincidence of hyperspectral reflectance nonnegativity (and its corresponding abundance) with nonnegative matrix factorization (NMF) methods, NMF has been widely applied to unmix hyperspectral images in recent years. However, many local minima persist because of the nonconvexity of the objective function. Thus, the nonnegativity constraint is not sufficient and additional auxiliary constraints should be applied to objective functions. In this paper, a new approach we call constrained multilayer NMF (CMLNMF), is proposed for hyperspectral data. In this approach, the mixed spectra are regarded as endmember signatures that has been contaminated by multiplicative noise. The purpose of CMLNMF is to eliminate noise by hierarchical processing until the endmember spectra are obtained. Also, the hierarchical processing is self-adaptive to make the algorithm more effective. Furthermore, in each layer two constraints are implemented on the objective function. One is sparseness on the abundance matrix and the other is minimum volume on the spectral matrix. The hierarchical processing separates the abundance matrix into a series of matrices that make the characteristic of sparseness more obvious and meaningful. The proposed algorithm is applied to synthetic data and real hyperspectral data for quantitative evaluation. According to the comparison with other algorithms, CMLNMF has better performance and provides effective solutions for blind unmixing of hyperspectral image data. Numéro de notice : A2017-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.14358/PERS.83.4.307 En ligne : https://doi.org/10.14358/PERS.83.4.307 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84590 [article]

Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in the city of Saskatoon (SK, Canada) / Li Shen in Open geosciences, vol 7 n° 1 (January 2015)  

Public [article]  inOpen geosciences > vol 7 n° 1 (January 2015) . - pp 27 - 39 Titre : Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in the city of Saskatoon (SK, Canada) Type de document : Article/Communication Auteurs : Li Shen, Auteur ; Xulin Guo, Auteur ; Kang Xiao, Auteur Année de publication : 2015 Article en page(s) : pp 27 - 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Canada [Termes IGN] image Aqua-MODIS [Termes IGN] image Landsat [Termes IGN] image Terra-MODIS [Termes IGN] milieu urbain [Termes IGN] régression linéaire [Termes IGN] température au sol [Termes IGN] température de l'air [Termes IGN] température de luminance Résumé : (auteur) The purpose of this study is to spatiotemporally explore the characteristics of urban temperatures based on multi-temporal satellite data and historical in situ measurements. As one of the most rapidly urbanized cities in Canada, Saskatoon (SK) was selected as our study area. Surface brightness retrieving, Pearson correlation, linear regression modeling, and buffer analysis were applied to different satellite datasets. The results indicate that both Landsat and MODIS data can yield pronounced estimations of daily air temperature with a significantly adjusted R2 of 0.803 and 0.518 at the spatial scales of 120m and 1000 m, respectively. MODIS monthly LST data is highly suitable for monitoring the trend of monthly urban air temperature throughout summer (June, July, and August) due to a high average R2 of 0.8 (P Numéro de notice : A2015-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/geo-2015-0005 En ligne : https://doi.org/10.1515/geo-2015-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77036 [article]