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Substance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
[article]
Titre : Substance dependence constrained sparse NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Min Fu, Auteur ; Xiaoqiang Lu, Auteur Année de publication : 2015 Article en page(s) : pp 2975 - 2986 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification barycentrique
[Termes IGN] état de l'art
[Termes IGN] factorisation
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuseRésumé : (Auteur) Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances. Recently, various methods have been proposed to incorporate sparse constraints into hyperspectral unmixing and achieve advanced performance. However, most of them ignore the complex distribution of substances in hyperspectral data so that they are only effective in limited cases. In this paper, the concept of substance dependence is introduced to help hyperspectral unmixing. Generally, substance dependence can be considered in a local region by K-nearest neighbors method. However, since substances of hyperspectral images are complicatedly distributed, number K of the most similar substances to each substance is difficult to decide. In this case, substance dependence should be considered in the whole data space, and the number of the K most similar substances to each substance can be adaptively determined by searching from the whole space. Through maintaining the substance dependence during unmixing, the abundances resulted from the proposed method are closer to the real fractions, which lead to better unmixing performance. The following contributions can be summarized. 1) The concept of substance dependence is proposed to describe the complicated relationship between substances in the hyperspectral image. 2) We propose substance dependence constrained sparse nonnegative matrix factorization (SDSNMF) for hyperspectral unmixing. Using SDSNMF, we meet or exceed state-of-the-art unmixing performance. 3) Adequate experiments on both synthetic and real hyperspectral data have been tested. Compared with the state-of-the-art methods, the experimental results prove the superiority of the proposed method. Numéro de notice : A2015-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365953 Date de publication en ligne : 13/01/2015 En ligne : https://doi.org/10.1109/TGRS.2014.2365953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76391
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 2975 - 2986[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Immaculada Dopido, Auteur ; Paolo Gamba, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2899 - 2912 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification dirigée
[Termes IGN] image hyperspectraleRésumé : (Auteur) Classification and spectral unmixing are two important techniques for hyperspectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperspectral image classification that naturally integrates the information provided by discriminative classification and spectral unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by spectral unmixing. In this case, we use a traditional spectral unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperspectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and spectral unmixing in adaptive fashion, offers an effective solution to improve- classification performance in hyperspectral scenes containing mixed pixels. Numéro de notice : A2015-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2366513 En ligne : https://doi.org/10.1109/TGRS.2014.2366513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77532
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2899 - 2912[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible HYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : HYCA: A new technique for hyperspectral compressive sensing Type de document : Article/Communication Auteurs : G. Martin, Auteur ; José M. Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2015 Article en page(s) : pp 2819 - 2831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] capteur hyperspectral
[Termes IGN] compression d'image
[Termes IGN] coordonnées géographiques
[Termes IGN] corrélation
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) Hyperspectral imaging relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest. In this paper, we exploit the high correlation existing among the components of the hyperspectral data sets to introduce a new compressive sensing methodology, termed hyperspectral coded aperture (HYCA), which largely reduces the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images, usually termed data cubes: 1) the spectral vectors live on a low-dimensional subspace; and 2) the spectral bands present high correlation in both the spatial and the spectral domain. The former property allows to represent the data vectors using a small number of coordinates. In this paper, we particularly exploit the high spatial correlation mentioned in the latter property, which implies that each coordinate is piecewise smooth and thus compressible using local differences. The measurement matrix computes a small number of random projections for every spectral vector, which is connected with coded aperture schemes. The reconstruction of the data cube is obtained by solving a convex optimization problem containing a data term linked to the measurement matrix and a total variation regularizer. The solution of this optimization problem is obtained by an instance of the alternating direction method of multipliers that decomposes very hard problems into a cyclic sequence of simpler problems. In order to address the need to set up the parameters involved in the HYCA algorithm, we also develop a constrained version of HYCA (C-HYCA), in which all the parameters can be automatically estimated, which is an important aspect for practical application of the algorithm. A series of experiments with simulated and real data shows the effectiveness of HYCA and C-HYCA, indicating their potential in real-world applications. Numéro de notice : A2015-520 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365534 En ligne : https://doi.org/10.1109/TGRS.2014.2365534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77527
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2819 - 2831[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : Hyperspectral image classification based on three-dimensional scattering wavelet transform Type de document : Article/Communication Auteurs : Yuan Yan Tang, Auteur ; Y. Lu, Auteur ; Haoliang Yuan, Auteur Année de publication : 2015 Article en page(s) : pp 2467 - 2480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] diffusion spatiale
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recent research has shown that utilizing the spectral-spatial information can improve the performance of hyperspectral image (HSI) classification. Since HSI is a 3-D cube datum, 3-D spatial filtering becomes a simple and effective method for extracting the spectral-spatial information. In this paper, we propose a 3-D scattering wavelet transform, which filters the HSI cube data with a cascade of wavelet decompositions, complex modulus, and local weighted averaging. The scattering feature can adequately capture the spectral-spatial information for classification. In the classification step, a support vector machine based on Gaussian kernel is used as a classifier due to its capability to deal with high-dimensional data. Our method is fully evaluated on four classic HSIs, i.e., Indian Pines, Pavia University, Botswana, and Kennedy Space Center. The classification results show that our method achieves as high as 94.46%, 99.30%, 97.57%, and 95.20% accuracies, respectively, when only 5% of the total samples per class is labeled. Numéro de notice : A2015-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2360672 En ligne : https://doi.org/10.1109/TGRS.2014.2360672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77524
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2467 - 2480[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data / Prabir Das in Geocarto international, vol 30 n° 5 - 6 (May - July 2015)
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Titre : Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data Type de document : Article/Communication Auteurs : Prabir Das, Auteur ; M.V.R. Seshasai, Auteur Année de publication : 2015 Article en page(s) : pp 686 - 700 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] capteur hyperspectral
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] rééchantillonnage
[Termes IGN] simulation numérique
[Termes IGN] surveillance de la végétationRésumé : (Auteur) The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI705), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI705, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement. Numéro de notice : A2015-307 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.973065#abstract Date de publication en ligne : 31/10/2014 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2014.973065#abstract Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76529
in Geocarto international > vol 30 n° 5 - 6 (May - July 2015) . - pp 686 - 700[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkActive learning with gaussian process classifier for hyperspectral image classification / Shujing Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkApport du LiDAR dans le géoréférencement d'images hyperspectrales en vue d'un couplage LiDAR/hyperspectral / Antoine Ba in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkLinear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkA physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkA technique for simultaneous visualization and segmentation of hyperspectral data / Abhimitra Meka in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkAn adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkCollaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkConstrained 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)PermalinkEmploying ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)Permalink