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Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1604 - 1617 Titre : Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary Type de document : Article/Communication Auteurs : Yubin Niu, Auteur ; Bin Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1604 - 1617 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification spectrale [Termes IGN] détection de cible [Termes IGN] image hyperspectrale Résumé : (Auteur) The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Numéro de notice : A2017-158 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2628085 En ligne : https://doi.org/10.1109/TGRS.2016.2628085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84695 [article]

Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1287 - 1303 Titre : Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery Type de document : Article/Communication Auteurs : Hanye Pu, Auteur ; Zhao Chen, Auteur ; Bin Wang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1287 - 1303 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] contrainte d'intégrité [Termes IGN] image hyperspectrale [Termes IGN] méthode des moindres carrés Résumé : (Auteur) Hyperspectral unmixing is an important issue in hyperspectral image processing. In this paper, we transform the unmixing problem into a constrained nonlinear least squares (CNLS) problem by introducing the abundance sum-to-one constraint, abundance nonnegative constraint, and bound constraints on nonlinearity parameters. The new CNLS-based algorithms assume that the mixing mechanism of each observed pixel can be described by two forms. One is a sum of linear mixtures of endmember spectra and nonlinear variations in reflectance, and the other is a joint mixture resulting from the linearity and nonlinearity in hyperspectral data. For the former, an alternating iterative optimization algorithm is developed to solve the problem of CNLS. As for the latter, the structured total least squares optimization approach is used to obtain the abundance vectors and nonlinearity parameters simultaneously. Current mixing models can be interpreted by either or both of these two mechanisms. A comparative analysis based on Monte Carlo simulations and real data experiments is conducted to evaluate the proposed algorithms and five other state-of-the-art algorithms. Experimental results show that the proposed algorithms give outstanding performance of hyperspectral nonlinear unmixing for both synthetic data and real hyperspectral images, as satisfactory accuracy in term of abundance fractions and low computational complexity are observed. Numéro de notice : A2015-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2336858 Date de publication en ligne : 30/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2336858 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75794 [article]

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065-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1157 - 176 Titre : A fully constrained linear spectral unmixing algorithm based on distance geometry Type de document : Article/Communication Auteurs : Hanye Pu, Auteur ; Wei Xia, Auteur ; Bin Wang, Auteur ; Geng-Ming Jiang, Auteur Année de publication : 2014 Article en page(s) : pp 1157 - 176 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] contrainte géométrique [Termes IGN] distance euclidienne [Termes IGN] estimation statistique [Termes IGN] méthode de Monte-Carlo Résumé : (Auteur) Under the linear spectral mixture model, hyperspectral unmixing can be considered as a convex geometry problem, in which the endmembers are located in the vertices of simplex enclosing the hyperspectral data set and the barycentric coordinates of observation pixels with respect to the simplex correspond to the abundances of endmembers. Based on distance geometry theory, in this paper we propose a new approach for abundance estimation of mixed pixels in hyperspectral images. With the endmember signatures, which is known a priori or can be obtained from the endmember extraction algorithms, the proposed method automatically estimates the abundances of endmembers at each pixel using convex geometry concepts and distance geometry constraints. In the algorithm, denoting the pairwise distances with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of the observation pixels. Another characteristic of this algorithm is that the optimal estimated points of observation pixels as well as the least distortion in geometric structure of original data set can be obtained with the distance geometry constraint. Simultaneously, the use of barycenter of simplex builds an accurate and efficient method to estimate endmembers with zero abundance and, as a result, the subsimplex containing the estimated points is obtained. A comparative study and analysis based on Monte Carlo simulations and real data experiments is conducted among the proposed algorithm and three state-of-the-art algorithms: fully constrained least squares (FCLS), FCLS computed using constrained sparse unmixing by variable splitting and augmented Lagrangian, and simplex-projection unmixing (SPU). The experimental results show that the proposed algorithm always provides the best unmixing accuracy and when the number of endmembers is not very large the algorithm has a lower computational complexity. Numéro de notice : A2014-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2248013 En ligne : https://doi.org/10.1109/TGRS.2013.2248013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32979 [article]

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065-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible