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Double constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
[article]
Titre : Double constrained NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Hao Wu, Auteur ; Yuan Yuan, Auteur Année de publication : 2014 Article en page(s) : pp 2746 - 2758 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] contrainte spectrale
[Termes IGN] factorisation
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] image hyperspectraleRésumé : (Auteur) Given only the collected hyperspectral data, unmixing aims at obtaining the latent constituent materials and their corresponding fractional abundances. Recently, many nonnegative matrix factorization (NMF)-based algorithms have been developed to deal with this issue. Considering that the abundances of most materials may be sparse, the sparseness constraint is intuitively introduced into NMF. Although sparse NMF algorithms have achieved advanced performance in unmixing, the result is still susceptible to unstable decomposition and noise corruption. To reduce the aforementioned drawbacks, the structural information of the data is exploited to guide the unmixing. Since similar pixel spectra often imply similar substance constructions, clustering can explicitly characterize this similarity. Through maintaining the structural information during the unmixing, the resulting fractional abundances by the proposed algorithm can well coincide with the real distributions of constituent materials. Moreover, the additional clustering-based regularization term also lessens the interference of noise to some extent. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art algorithms. Numéro de notice : A2014-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2265322 En ligne : https://doi.org/10.1109/TGRS.2013.2265322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33166
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2746 - 2758[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014051A RAB Revue Centre de documentation En réserve L003 En circulation
Exclu du prêtHyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
[article]
Titre : Hyperspectral image denoising with a spatial–spectral view fusion strategy Type de document : Article/Communication Auteurs : Qiangqiang Yuan, Auteur ; Liangpei Zhang, Auteur ; Huanfeng Shen, Auteur Année de publication : 2014 Article en page(s) : pp 2314 - 2325 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image spatialeRésumé : (Auteur) In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the denoising results of the two views together, so that the denoising result is improved. A number of experiments illustrate that the proposed approach can produce a better denoising result than both the individual spatial and spectral view denoising results. Numéro de notice : A2014-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2259245 Date de publication en ligne : 15/07/2013 En ligne : https://doi.org/10.1109/TGRS.2013.2259245 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33162
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2314 - 2325[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014051A RAB Revue Centre de documentation En réserve L003 En circulation
Exclu du prêtSlow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
[article]
Titre : Slow feature analysis for change detection in multispectral imagery Type de document : Article/Communication Auteurs : Chen Wu, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 2858 - 2874 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de variance
[Termes IGN] détection de changement
[Termes IGN] image Landsat-ETM+
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] pixel
[Termes IGN] varianceRésumé : (Auteur) Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods. Numéro de notice : A2014-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2266673 En ligne : https://doi.org/10.1109/TGRS.2013.2266673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33167
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2858 - 2874[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014051A RAB Revue Centre de documentation En réserve L003 En circulation
Exclu du prêtWetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar / Lian P. Rampi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)
[article]
Titre : Wetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar Type de document : Article/Communication Auteurs : Lian P. Rampi, Auteur ; Joseph F. Knight, Auteur ; Keith C. Pelletier, Auteur Année de publication : 2014 Article en page(s) : pp 439 - 449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] image radar
[Termes IGN] marais
[Termes IGN] Minnesota (Etats-Unis)Résumé : (Auteur) This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer's and user's accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods. Numéro de notice : A2014-243 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.5.439 En ligne : https://doi.org/10.14358/PERS.80.5.439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33146
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 5 (May 2014) . - pp 439 - 449[article]Bayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
[article]
Titre : Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Christopher Ratto, Auteur ; Kenneth D. Morton, Auteur ; Leslie M. Collins, Auteur ; Peter A. Torrione, Auteur Année de publication : 2014 Article en page(s) : pp 1969 - 1981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification contextuelle
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] rayonnement infrarougeRésumé : (Auteur) Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. Numéro de notice : A2014-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257175 En ligne : https://doi.org/10.1109/TGRS.2013.2257175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33170
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1969 - 1981[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkImpact of signal contamination on the adaptive detection performance of local hyperspectral anomalies / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkProgressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkAbove ground biomass estimation in an African tropical forest with lidar and hyperspectral data / Gaia Vaglio Laurin in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkAutomated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B / Chabitha Devarj in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkEfficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkSemi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)PermalinkUL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkUn vaste champ d'applications / Françoise de Blomac in DécryptaGéo le mag, n° 155 (01/03/2014)PermalinkAutomatic registration of optical imagery with 3D LiDAR data using statistical similarity / Ebadat Ghanbari Parmehr in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkDetecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm / Abduwasit Ghulam in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkEstimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China / Liguo Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkA GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation / Xiran Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkNonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization / Naoto Yokoya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkStructured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkAssessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)PermalinkCollaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)PermalinkComparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)Permalink