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Toward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan 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 269 - 280 Titre : Toward optimum fusion of thermal hyperspectral and visible images in classification of urban area Type de document : Article/Communication Auteurs : Farhad Samadzadegan, Auteur ; Hadiseh Hasani, Auteur ; Peter Reinartz, Auteur Année de publication : 2017 Article en page(s) : pp 269 - 280 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande visible [Termes IGN] bati [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] fusion d'images [Termes IGN] géostatistique [Termes IGN] image hyperspectrale [Termes IGN] image thermique [Termes IGN] indice de végétation [Termes IGN] morphologie [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau routier [Termes IGN] zone urbaine Résumé : (Auteur) Recently, classification of urban area based on multi-sensor fusion has been widely investigated. In this paper, the potential of using visible (VIS) and thermal infrared (TIR) hyperspectral images fusion for classification of urban area is evaluated. For this purpose, comprehensive spatial-spectral feature space is generated which includes vegetation index, differential morphological profile (DMP), attribute profile (AP), texture, geostatistical features, structural feature set (SFS) and local statistical descriptors from both datasets in addition to original datasets. Although Support Vector Machine (SVM) is an appropriate tool in the classification of high dimensional feature space, its performance is significantly affected by its parameters and feature space. Cuckoo search (CS) optimization algorithm with mixed binary-continuous coding is proposed for feature selection and SVM parameter determination simultaneously. Moreover, the significance of each selected feature category in the classification of a specific object is verified. Accuracy assessment on two subsets shows that stacking of VIS and TIR bands can improve the classification performance to 87 percent and 82 percent for two subsets, compare to VIS image (72 percent and 80 percent) and TIR image (50 percent and 56 percent). However, the optimum results obtained based on the proposed method which gains 94 percent and 92 percent. Furthermore, results show that using TIR beside VIS image improves classification accuracy of roads and buildings in urban area. Numéro de notice : A2017-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.4.269 En ligne : https://doi.org/10.14358/PERS.83.4.269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84589 [article]

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]