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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)
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Titre : Hyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation Type de document : Article/Communication Auteurs : Allan W. Yarbrough, Auteur ; Michael J. Mendenhall, Auteur ; Richard K. Martin, Auteur ; Steven T. Fiorino, Auteur Année de publication : 2014 Article en page(s) : pp 2029 - 2039 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'erreur
[Termes IGN] données météorologiques
[Termes IGN] erreur de classification
[Termes IGN] estimation statistique
[Termes IGN] filtrage numérique d'image
[Termes IGN] filtre spectral
[Termes IGN] humidité de l'air
[Termes IGN] image hyperspectrale
[Termes IGN] transfert radiatif
[Termes IGN] vapeur d'eauRésumé : (Auteur) Accurate target detection and classification in hyperspectral imagery require that the spectral measurements by the imager match as closely as possible the known “true” target as collected under controlled conditions and stored in a target database. Therefore, the effect of the radiation source and the atmosphere must be factored out of the result before detection is attempted. Our objective is to evaluate detection error due to the error in estimating the atmospherics. We apply a range of atmospheric water vapor profiles, corresponding to different relative humidities, to a model-based prediction of the radiative transfer to examine the effect of water vapor on simulated hyperspectral imagery. These profiles are taken from known distribution percentiles as obtained from historic meteorological measurements close to the sites being simulated. We quantify the expected detection error for the adaptive matched filter, as measured by the receiver operating characteristic (ROC) and the area under the ROC curve, given the range of atmospheric conditions in the historic profile. We discover that, depending on the target, and given the uncertainty as to the true atmospheric conditions, detection rates improve on average across the historic range when we assume the atmospheric profile is at the 35th percentile of atmospheric relative humidity instead of the 50th percentile. Numéro de notice : A2014-269 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257797 En ligne : https://doi.org/10.1109/TGRS.2013.2257797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33172
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 2029 - 2039[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Impact 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)
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Titre : Impact of signal contamination on the adaptive detection performance of local hyperspectral anomalies Type de document : Article/Communication Auteurs : Stefania Matteoli, Auteur ; Marco Diani, Auteur ; Giovanni Corsini, Auteur Année de publication : 2014 Article en page(s) : pp 1948 - 1968 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] contamination
[Termes IGN] covariance
[Termes IGN] dégradation du signal
[Termes IGN] détection d'anomalie
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] rapport signal sur bruit
[Termes IGN] signature spectrale
[Termes IGN] variabilitéRésumé : (Auteur) The effects of signal contamination of secondary data are investigated in the framework of adaptive target detection in remotely sensed hyperspectral images. In contrast to previous studies on signal contamination, the focus of this paper is the detection of targets with unknown spectral signatures (i.e., anomalies) and adaptive detection methods based on a local estimation of the background covariance matrix. Contamination due to the target signal is expected to have a more severe impact when the number of secondary data is limited. An analytical model for signal contamination is developed that allows variability in the extent of contamination. Several parameters, such as the contamination fraction of secondary data and the contaminating signal energy, are introduced, and a contaminating signal-to-interference-plus-noise ratio is derived as an objective measure of contamination. The proposed model is employed to experimentally evaluate signal contamination effects and the impact of its variability on the performance of adaptive detection of local anomalies. The outcomes of the experimental study are substantiated by validation with real hyperspectral data. The results obtained highlight the relevance that the impact of signal contamination, assessed with respect to different system parameters, may have for practical applications. This paper represents a starting point for the development of detection performance forecasting models that consider signal contamination. Numéro de notice : A2014-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2256915 En ligne : https://doi.org/10.1109/TGRS.2013.2256915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33169
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1948 - 1968[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Progressive 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)
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Titre : Progressive band selection of spectral unmixing for hyperspectral imagery Type de document : Article/Communication Auteurs : Chein-I Chang, Auteur ; Keng-Hao Liu, Auteur Année de publication : 2014 Article en page(s) : pp 2002 - 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectraleRésumé : (Auteur) A new band selection (BS), called progressive BS (PBS) of spectral unmixing for hyperspectral imagery is being presented. It is quite different from the traditional BS in the sense that the former adapts the number of selected bands, p to various endmembers used for spectral unmixing, while the latter fixes the value of p at a constant for all endmembers. Due to the fact that different endmembers post various levels of difficulty in discrimination, each endmember should have its own custom-selected bands to specify its spectral characteristics. In order to address this issue, p is composed of two values, one value determined by virtual dimensionality to accommodate each of endmembers and the other is determined by a new concept of band dimensionality allocation to account for discrminability among endmembers. In order to find appropriate bands to be used for PBS, band prioritization and band de-correlation are included to rank bands according to significance of band information and to remove interband redundancy, respectively. As a result, spectral unmixing can be performed progressively by selecting different bands for various endmembers, a task that the traditional BS cannot accomplish. The effectiveness and advantages of using PBS over BS are also demonstrated by experiments. Numéro de notice : A2014-268 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257604 En ligne : https://doi.org/10.1109/TGRS.2013.2257604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33171
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 2002 - 2017[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Above 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)
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Titre : Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data Type de document : Article/Communication Auteurs : Gaia Vaglio Laurin, Auteur ; Qi Chen, Auteur ; Jeremy A. Lindsell, Auteur ; David A. Coomes, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 49 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Afrique tropicale
[Termes IGN] bilan du carbone
[Termes IGN] biomasse
[Termes IGN] données lidar
[Termes IGN] forêt tropicale
[Termes IGN] image hyperspectrale
[Termes IGN] modélisation de la forêtRésumé : (Auteur) The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R2 = 0.70) improved the model based on lidar alone (R2 = 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands with vegetation indexes resulted in a smaller improvement (R2 = 0.67). Hyperspectral bands had limited predictive power (R2 = 0.36) when used alone. This analysis proves the efficiency of using PLSR with small-footprint lidar and high resolution hyperspectral data in tropical forests for biomass estimation. Results also suggest that high quality ground truth data is crucial for lidar-based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area. Numéro de notice : A2014-124 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.01.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.01.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33029
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 49 - 58[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Automated 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)
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Titre : Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B Type de document : Article/Communication Auteurs : Chabitha Devarj, Auteur ; Chintan A. Shah, Auteur Année de publication : 2014 Article en page(s) : pp 13 - 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chambre DTC
[Termes IGN] correction géométrique
[Termes IGN] géoréférencement direct
[Termes IGN] image à haute résolution
[Termes IGN] image CBERS
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] orthorectificationRésumé : (Auteur) China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Numéro de notice : A2014-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33026
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 13 - 24[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Efficient, 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)
PermalinkPermalinkAutomatic 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)
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